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Location:         Mumbai & Pune

Job Type:         Full-time, Remote/Hybrid (2/3 split)

Industry:         Healthcare

Language Requirement:

• Proficient in English with strong communication and documentation skills

Experience Level:

• Intermediate to Advanced
• 5+ years of experience

Technical Requirements:

• Expertise in SQL, data engineering, data warehousing, data integration, and data modeling best practices
• Experience with error handling and error logging
• Proficiency with ETL tools such as Azure Data Factory and SSIS
• Experience with API data integration
• Knowledge of Power Automate and Power Apps preferred
• Experience with Power BI or other business intelligence tools is a plus

Responsibilities:

• Design, develop, and maintain robust ETL processes
• Ensure data accuracy and integrity across databases and reporting systems
• Collaborate with stakeholders to understand data requirements and deliver solutions
• Implement data models and optimize data retrieval processes
• Create dashboards and reports for business intelligence insights
• Address client issues with innovative problem-solving and best-practice solutions
• Manage time effectively to meet project deadlines in a remote work environment
• Capture requirements, design technical solutions, develop and test reports, and maintain the health of BI environments

Skills:

• Excellent time management and organizational abilities
• Independent self-starter with a proactive approach to tasks
• Strong problem-solving skills with a creative mindset
• Capable of working autonomously and as part of a small team

Education:

• Bachelor's Degree in Computer Science, Mathematics, Healthcare Management, Engineering, or
• 5+ years of relevant experience in lieu of a degree

Job Overview:

The AI Data Engineer is responsible for building and operating high‑quality, governed, and AI‑ready data pipelines that power enterprise GenAI and agent‑based use cases. This role focuses on preparing data for retrieval‑augmented generation (RAG), managing embeddings and vector indexes, and ensuring data quality, lineage, and compliance across the AI platform. As part of the AI CoE Technology pod, the AI Data Engineer enables rapid, responsible AI development by delivering reusable, scalable data foundations. This is a hands‑on individual contributor role within the AI CoE – Technology pod, working closely with AI Platform Engineers and AI Engineers to support both shared platform capabilities and priority AI use cases. The role is intentionally centralized to avoid fragmented data pipelines and to ensure consistent governance, quality, and reuse across the enterprise.

Major Responsibilities:

• Design, build, and maintain data pipelines that ingest, transform, and curate structured and unstructured data for AI use cases.
• Prepare RAG‑ready datasets by applying metadata enrichment, chunking, normalization, and document parsing patterns aligned to platform standards.
• Partner with source system teams and domain SMEs to understand data semantics and ensure accurate representation for AI consumption.

• Create and maintain embedding pipelines, including generation, refresh, and lifecycle management.
• Own vector index maintenance, including re‑indexing strategies, performance tuning, and cleanup of stale or unused embeddings.
• Support knowledge grounding for AI agents by ensuring source attribution, consistency, and traceability.

• Implement data quality checks, validation rules, and monitoring to ensure accuracy, completeness, and reliability of AI datasets.
• Ensure all AI data pipelines comply with enterprise data governance, privacy, and information management policies, including support for regulated and sensitive data use cases.
• Collaborate with Architecture, Security, and Information Governance partners to align data handling with approved AI patterns and risk controls.

• Support AI Engineers during onboarding and troubleshooting by diagnosing data issues that affect agent behavior or retrieval accuracy.
• Contribute reusable data patterns, templates, and documentation to accelerate future AI use cases.
• Participate in platform support activities defined in the AI CoE RACI, particularly those related to data grounding and vector maintenance.

• Optimize data and embedding pipelines for performance, scalability, and cost efficiency, in partnership with Platform Engineers.
• Monitor data freshness and usage trends to recommend retirement, refresh, or enhancement of datasets supporting AI agents.

Qualifications:

• Bachelor’s degree in computer science, Engineering, Data Science, or a related technical discipline OR equivalent combination of education and relevant experience.
• Demonstrated experience designing and operating production-grade data pipelines in an enterprise environment.
• Experience working with unstructured data (documents, text, PDFs) and preparing data for analytics, ML, or AI use cases.
• Working knowledge of embeddings, vector databases, and retrieval patterns used in modern AI and GenAI solutions.
• Strong understanding of data quality, lineage, and governance concepts.

Additional Licensing, Certifications, Registrations:

• Professional certification(s) in area of expertise a plus

-AWS Machine Learning Specialty, Azure AI Engineer Associate, or equivalent cloud certifications.

-Databricks and/or Snowflake certifications

Knowledge, Skills, and Abilities:

• Strong hands-on experience designing and operating data pipelines for analytics, ML, or AI workloads.
• Experience working with unstructured data (documents, PDFs, text) and preparing it for downstream AI or search use cases.
• Knowledge of embeddings, vector databases, and retrieval patterns used in RAG or knowledge-based AI systems.
• Strong understanding of data quality, lineage, and governance concepts in enterprise environments.
• Experience supporting GenAI or agentic AI platforms in a regulated enterprise environment (e.g., healthcare, financial services).
• Familiarity with cloud-native data services and AI platforms commonly used for enterprise AI enablement.
• Experience partnering with platform and application teams in a federated or hub-and-spoke operating model.
• Understanding of healthcare compliance standards (HIPAA, HITRUST) and ethical AI practices (bias, explainability,data privacy).
• Ability to collaborate effectively with cross-functional teams and translate business requirements into technical solutions.
• Strong problem-solving and innovation mindset, with the ability to adapt generative AI to real-world challenges in healthcare and ability to adapt to and adapt to evolving priorities and technologies
• Familiarity with governance and compliance frameworks relevant to healthcare (HIPAA, SOC 2, HITRUST) preferred

Overview

We need a data enthusiast who pairs sharp analytical capabilities with the leverage of modern AI tools. As our organization harmonizes multiple disparate systems, data sources, and analytics platforms into a single operational home, this role will lead and orchestrate the collection, cohesion, and maintenance of our core data models (unified accounts, contacts, candidates, deals, activity, and work records).

You will design and oversee the foundational data layer that allows future AI agents to read seamlessly across all entity data without an integration tax, setting the groundwork for our incoming ERP consolidation.

What You Own

• Canonical Data Model: Define entity structures, relationships, and identity-resolution logic across accounts, contacts, candidates, deals, projects, and work records.
• Validation at Ingest: Establish upfront validation rules that prevent low-quality records from entering systems, shifting focus from post-hoc error reporting to preventative data hygiene.
• Integration Layer Coordination: Collaborate with full-stack engineers to maintain an adaptable integration layer, ensuring vendor replacements require simple configuration rather than a full code rewrite.
• Reporting & Dashboards Foundation: Build and govern the modeled semantic layer beneath our Power BI transition, unifying legacy reports and setting up actionable dashboards for the future ERP.
• Agent Data Access: Architect clean, permissioned, and documented read paths so internal AI agents consume structured data directly rather than scraping front-end source systems.
• Data Quality Ownership: Serve as the direct point of contact and owner for recurring systemic data defects.
• Migration Strategy: Lead data migration paths off retired legacy systems while preserving historical continuity and audit trails.
• Lean AI & Continuous Improvement: Apply Lean principles and AI tools to reduce data failure points, detect early warning indicators, and define KPIs/SLAs to optimize organizational performance.
• Unstructured Data Insights: Monitor, analyze, and process speech, text, and sentiment analysis inputs across business channels.

Requirements

• 4+ years of hands-on experience in data and analytics, featuring heavy data-modeling responsibilities.
• Proven track record of designing a canonical data model across multiple source systems, with the ability to articulate trade-offs in identity resolution and deduplication.
• Deep knowledge of PostgreSQL (schema design, normalization trade-offs, database migrations, query performance tuning).
• SQL proficiency for data exploration, auditing, reconciliation, and cross-database validation.
• Python expertise for developing transformation scripts and data pipelines.
• Demonstrated experience building and supporting integrations with commercial SaaS APIs (handling rate limits, unexpected schema updates, and partial failures).
• Strong understanding of data-layer security, designing models where authorization and field-level restrictions are enforced structurally.
• Proactive adoption of AI tools to accelerate pipeline creation, audit data quality, and enrich datasets.
• Inherent curiosity and rigor for inspecting, validating, and establishing trust in business metrics.

Strong Preferences

• Direct familiarity with core enterprise data models across ERP, HRIS, WFM, or CRM platforms (e.g., NetSuite, SAP, Workday, Salesforce, Genesys, Oracle, or Aspect).
• Experience using tools like dbt (or comparable transformation and automated testing frameworks).
• Hands-on experience working with Tableau, Power BI, or similar analytics stacks, specifically using AI to extract insight from structured datasets.
• Practical experience consolidating legacy commercial systems onto unified or in-house platforms.
• Exposure to recruitment, workforce, or operations data, including handling candidate PII and data retention requirements.
• Prior experience preparing database schemas or data warehouse layers specifically to feed LLMs, AI workflows, or autonomous agent routines.

ABOUT THE COMPANY

At Parable Associates, we specialize in transforming complex data into clear, actionable insights for our clients across various industries. We’re not just another business intelligence company; we’re storytellers, turning data into meaningful narratives that empower organizations to make informed decisions faster. Our innovative approach, combined with a passion for results, makes us the trusted partner for organizations seeking to optimize their data operations and unlock new insights.

ROLE

Are you excited about building the future of data solutions and leveraging cutting-edge cloud technologies? At Parable Associates, you’ll be at the forefront of developing and optimizing data pipelines that drive impactful insights for our clients. You'll do more than just develop data solutions—you’ll be a key partner in shaping how organizations utilize their data to drive innovation and success.

In this role, you'll work closely with both internal teams and clients to design and implement cloud-based data architectures using Microsoft Fabric and similar cloud platforms. You’ll guide the process from data ingestion to transformation and ensure data quality, governance, and efficiency. If you're passionate about solving complex challenges, have a strong understanding of cloud data platforms, and are eager to make an impact, we’d love to have you join our team.

RESPONSIBILITIES

• Design, develop, and maintain scalable, secure, and high-performing data pipelines using Microsoft Fabric, Azure Data Factory, PySpark, and Databricks.
• Create and manage data pipelines to ingest, process, and transform data from various sources into the lakehouse.
• Collaborate with data architects, business analysts, and other stakeholders to understand data requirements and translate them into scalable solutions.
• Build and optimize data models and data warehousing solutions to support business intelligence and advanced analytics, implementing techniques such dimensional modeling, normalization, creating surrogate keys, utilizing Slowly Changing Dimensions, partitioning, and aggregation.
• Perform data transformation and pipeline orchestration using Fabric Notebooks to cleanse and prepare data for report consumption.
• Implement and maintain security measures to protect data assets
• Ensure data quality, integrity, and governance across all stages of the data lifecycle.
• Optimize performance of data ingestion, transformation, and loading processes.
• Proactively identify areas for process improvement, automation, and cost efficiency within the data pipeline infrastructure.
• Provide technical mentorship to junior engineers and collaborate on the adoption of best practices in data engineering.
• Maintain comprehensive documentation of data systems, processes, and workflows.

RESULTS

• Reliable, efficient data pipelines that deliver clean, timely data to stakeholders.
• Scalable, high-performance data platforms that meet client requirements for analytics and reporting.
• Streamlined data workflows that improve time-to-insight for business intelligence initiatives.
• High client satisfaction through seamless data solutions that solve complex challenges.

REQUIREMENTS

• 5+ years of experience in data engineering or a similar role, with a focus on cloud-native technologies.
• Experience in Microsoft Fabric or Azure Data Factory, with hands-on experience designing data pipelines.
• Experience using PySpark in big data processing and transformation
• Strong understanding of data modeling, ETL and ELT processes, and building data warehousing solutions.
• Strong knowledge of modern data architecture principles and best practices.
• Excellent problem-solving skills with the ability to troubleshoot complex data challenges.
• Bachelor’s degree in computer science, information systems, or a related field.
• Highlight motivated, self-starter who can work independently and take initiative to drive projects forward

PREFERRED

• Experience with cloud platforms such as Microsoft Azure.
• Familiarity with Power BI for data visualization and reporting.
• Knowledge of data governance and security best practices.

We are seeking a highly capable data quality analyst to develop procedures to enhance the accuracy and integrity of our organization's data. You will be performing data analysis, collaborating with database developers to enhance data collection and storage procedures, and preparing data analysis reports.

To ensure success as a data quality analyst, you should exhibit extensive knowledge of data analysis techniques and experience in a similar role. A top-notch data quality analyst will be someone whose data analysis expertise results in reliable information for company executives.

Data Quality Analyst Responsibilities:

• Performing statistical tests on large datasets to determine data quality and integrity.
• Evaluating system performance and design, as well as its effect on data quality.
• Collaborating with database developers to improve data collection and storage processes.
• Running data queries to identify coding issues and data exceptions, as well as cleaning data.
• Gathering data from primary or secondary data sources to identify and interpret trends.
• Reporting data analysis findings to management to inform business decisions and prioritize information system needs.
• Documenting processes and maintaining data records.
• Adhering to best practices in data analysis and collection.
• Keeping abreast of developments and trends in data quality analysis.

Data Quality Analyst Requirements:

• Bachelor's degree in statistics, mathematics, computer science, information management, or similar.
• At least 5 years of experience in data analysis.
• Proficiency in programming languages, including Structured Query Language (SQL) and JavaScript.
• In-depth knowledge of statistical methods and tests.
• Extensive experience with statistical packages, such as MS Excel, SAS, and SPSS
• Exceptional analytical skills.
• Advanced problem-solving skills.
• Knowledge of best practices in data analysis.
• Excellent interpersonal and communication skills.

Fountain sells agentic software. We intend to run on it too.

Our data platform is healthy and owned. dbt and Dagster on Kubernetes, ClickHouse Cloud as the primary analytical store, CDC off Postgres and MongoDB source systems, a long tail of third-party sources, and Snowflake, Redshift, BigQuery, and object-store lakes around the edges. Engineers on the team own those models and pipelines today, and they’ll keep owning them.

This role is different. You own the agentic buildout itself, in two halves.

First, how our own team works. We want the data team building agentically by default: agents that author and test models, extend pipelines, catch breakage and repair it, keep documentation and contracts honest. Someone has to design that system. The harnesses, the context and tooling agents work through, the review and CI workflows that make agent-written code safe to merge, the evaluations that tell us whether the output can be trusted. Other engineers own the models. You own the machinery that changes how they get built, and that machinery reaches upstream into the product, where most data problems actually start.

Second, how the rest of the company works. The same capability, pointed outward. A CSM, a finance analyst, a PM, or a support lead should be able to ask a real question and get a trustworthy answer without waiting in a queue. That means semantic context, skills and MCP surfaces, access boundaries, evaluation, and a clear-eyed view of what the agent should refuse to answer. It also means working closely with the people who’ll use it, because tooling nobody adopts is worth nothing.

At its core this is still a data and analytics engineering job. You need enough depth in modeling, dbt, and orchestration to build credibly for engineers who do it all day, and to know when agent-generated output is subtly wrong. The difference is what you’re accountable for: the leverage, not the DAG.

Be aware that this is as much a change problem as an engineering problem. Building the capability is half of it. Getting a team, and then a company, to genuinely work differently is the other half, and it’s the half that decides whether this role succeeds.

We’re moving quickly. This role is a bet that we can change how we work in weeks rather than quarters, and we’re staffing it accordingly.

What you’ll do:

• Design and build the agentic development workflow for the data team. Agent-authored models and pipeline changes, the context and tooling those agents operate through, PR and review patterns, CI that catches what agents get wrong, the guardrails that make it safe to run against production, and the standards that make any of it repeatable across a team.
• Push that reach upstream into the product. Build the context and tooling that shape how features get conceived and built in the first place, and verify data architecture while changes are in development. Most data problems are cheapest to fix at the root, before a dev PR lands, which means working inside Engineering and Product rather than downstream of them.
• Build the evaluation layer. Regression suites, correctness checks, lineage awareness, and observability on the agents themselves. Nobody adopts an agentic workflow they can’t verify, so this is foundational rather than a follow-up.
• Build agentic analytics capability for the company. Semantic context, skills, MCP surfaces, access controls, and query and cost guardrails that let non-technical teams get answers they can act on, along with the honest scoping of what the system won’t answer.
• Drive adoption. Work alongside the engineers on the data team and stakeholders across GTM, Finance, Product, and Support, teaching the patterns, watching where people get stuck, and closing the gap between what you built and how people actually work.
• Stay hands-on in the platform. You won’t own the models and pipelines, but you’ll work in that codebase constantly, and you should be able to fix what you find.

What you should bring:

• 5+ years in data engineering, analytics engineering, or a closely related role, or equivalent depth arrived at another way. What you’ve built and operated matters more to us than the number.
• Real depth in analytical data modeling and SQL. You can read a normalized transactional schema, design something that answers business questions without falling over, and spot when a generated model is subtly wrong.
• Hands-on production experience with dbt and an orchestrator. Dagster is what we run; Airflow, Prefect, or Temporal transfers fine. You should know the operational reality of a large DAG, not just the syntax.
• Strong Python and the software engineering habits that go with it: version control, testing, code review, CI/CD.
• You have shipped agentic or LLM-powered systems to production. Tool and function calling, context and retrieval design, orchestrating multi-step workflows, and evaluating all of it. You should be able to walk through something you built, how you measured whether it worked, and where it failed. This is the requirement we care most about.
• Sound judgment about data governance and PII in a multi-tenant environment. We handle applicant and worker data across 75+ countries, and exposing it through an LLM surface raises the stakes on getting access boundaries right.
• You can bring people with you. This role changes how the data team, product engineers, and other departments do their work. That requires listening well, teaching patiently, and being persuasive without a title that makes anyone do anything.

Nice to have:

• Depth in a columnar or MPP warehouse, especially ClickHouse Cloud and ClickPipes; Snowflake, BigQuery, or Redshift also translates
• Building with Claude: the API, Claude Code, the Agent SDK, MCP servers, or subagent patterns
• AWS, Kubernetes, and infrastructure as code, enough to debug your own deployment
• Semantic layer and BI tooling (Omni, Looker, dbt Semantic Layer, Cube)
• LLM observability and evaluation tooling (Langfuse, Braintrust, or equivalent)
• Streaming and CDC internals (Debezium, Kafka, Kinesis)
• Having led a technical practice change on a team that was skeptical at the start
• HR tech, high-volume hiring, workforce management, or another domain with heavy operational data

What success looks like:

These are weeks, not quarters. That’s the point, and it’s possible because you won’t be carrying the platform while you do it. The models and pipelines have owners, so your time goes into building rather than inheriting.

Week 1 — you’ve shipped. A small agent-assisted change, merged to production. You learn our stack by building in it.

Week 2 — the first agentic workflow is running against production. Agent-authored changes moving through a review path you designed, with the CI and guardrails that make merging them safe.

Week 4 — engineers other than you are using it daily, the evaluation layer is catching regressions before a human does, and the first agentic analytics capability is in front of real users outside the data team, rough edges and all.

Week 8 — agent-initiated is the default path for at least one entire class of data work, the patterns are documented well enough that adoption no longer depends on you being in the room, internal agentic analytics is live for at least one department with measurement behind it, and the rate at which we add trustworthy data capability is no longer bounded by the size of the data team.

Full-Stack Data Platform Engineer

$80k – $160k • 0.0% – 0.4%

We’re building Reflow, a workforce and workflow intelligence platform that helps teams deeply understand how work gets done. As we scale, the data we collect is becoming richer and more complex. We need a data platform engineer to help us design and operate the systems that turn that data into intelligence — powering analytics, workflow insights, and economic modeling.

What you’ll do

• Design, implement, and maintain a scalable data warehouse (BigQuery, Snowflake, Redshift, or similar).
• Develop and optimize ETL pipelines to ingest data from APIs and internal systems.
• Model and manage datasets to support flexible analytics and product features.
• Collaborate with engineering team to improve data mining and analytics performance.
• Build and maintain dashboards and visualization tools (Metabase, Tableau, Power BI) to enable internal and external insights.
• Ensure data reliability, cost efficiency, and performance optimization across environments.
• Implement event-based pipelines for real-time analytics and reporting.
• Contribute to data governance, privacy, and security best practices.

Who you are

• Experienced in data warehouse architecture and scalable analytics infrastructure.
• Strong with SQL, data modeling, and pipeline performance optimization.
• Hands-on with ETL tools, data ingestion frameworks, and cloud-based data operations.
• Capable of balancing technical depth with real-world impact — you build systems people actually use.
• Comfortable navigating tradeoffs between cost, scalability, and complexity.
• Curious about how data translates into insights, decisions, and automation.

Bonus points

• Experience with Python for analytics and data wrangling.
• Familiarity with AI/ML-driven analytics or predictive modeling.
• Exposure to real-time or streaming data architectures.
• Understanding of data governance, compliance, and secure cloud operations.

Why join

You’ll build the backbone of Reflow’s intelligence layer, the systems that make our data usable, fast, and insightful. You’ll work directly with founders and engineers across analytics, infrastructure, and product. This is a high-impact technical role that sits at the intersection of scale, performance, and strategy.

We’re open to part or full-time. Ideal for builders who care about performance and precision at scale.

Full-Stack Data Platform Engineer

$80k – $160k • 0.0% – 0.4%

We’re building Reflow, a workforce and workflow intelligence platform that helps teams deeply understand how work gets done. As we scale, the data we collect is becoming richer and more complex. We need a data platform engineer to help us design and operate the systems that turn that data into intelligence — powering analytics, workflow insights, and economic modeling.

What you’ll do

• Design, implement, and maintain a scalable data warehouse (BigQuery, Snowflake, Redshift, or similar).
• Develop and optimize ETL pipelines to ingest data from APIs and internal systems.
• Model and manage datasets to support flexible analytics and product features.
• Collaborate with engineering team to improve data mining and analytics performance.
• Build and maintain dashboards and visualization tools (Metabase, Tableau, Power BI) to enable internal and external insights.
• Ensure data reliability, cost efficiency, and performance optimization across environments.
• Implement event-based pipelines for real-time analytics and reporting.
• Contribute to data governance, privacy, and security best practices.

Who you are

• Experienced in data warehouse architecture and scalable analytics infrastructure.
• Strong with SQL, data modeling, and pipeline performance optimization.
• Hands-on with ETL tools, data ingestion frameworks, and cloud-based data operations.
• Capable of balancing technical depth with real-world impact — you build systems people actually use.
• Comfortable navigating tradeoffs between cost, scalability, and complexity.
• Curious about how data translates into insights, decisions, and automation.

Bonus points

• Experience with Python for analytics and data wrangling.
• Familiarity with AI/ML-driven analytics or predictive modeling.
• Exposure to real-time or streaming data architectures.
• Understanding of data governance, compliance, and secure cloud operations.

Why join

You’ll build the backbone of Reflow’s intelligence layer, the systems that make our data usable, fast, and insightful. You’ll work directly with founders and engineers across analytics, infrastructure, and product. This is a high-impact technical role that sits at the intersection of scale, performance, and strategy.

We’re open to part or full-time. Ideal for builders who care about performance and precision at scale.

Role Summary

Own and grow the Texas & South-Central territory by selling technical workforce solutions into data center construction projects. This is a field-based, hunter role focused on building a pipeline from scratch, developing relationships with key contractors and developers, and closing long-term, high-value service agreements.

Responsibilities

• Build, manage, and convert a pipeline of data center construction opportunities
• Develop relationships with General Contractors (GCs), Electrical Contractors (ECs), and developers
• Own the full sales cycle: prospecting, meetings, proposals, and closing
• Track active projects, bid cycles, and procurement timelines in the region
• Represent the company at job sites, client meetings, and industry events
• Partner with internal delivery teams to position and win work

Requirements

• 5–10+ years in sales or business development roles
• Experience selling staffing, workforce solutions, telecom, or construction services
• Proven track record closing 6–7 figure deals
• Strong ability to build relationships and generate new business
• Comfortable working in a field-based role with regional travel

Territory Overview

Texas & South Central is one of the fastest-growing data center regions, including:

• Dallas–Fort Worth (major hyperscaler cluster)
• Abilene, San Antonio, South Dallas
• Regional coverage: Louisiana, Arkansas, Mississippi, Oklahoma

DFW is the hub — most projects are drivable with occasional regional travel.

Nice to Have

• Existing relationships with contractors or developers in Texas
• Experience selling into data center or large infrastructure projects
• Familiarity with fiber, structured cabling, or low-voltage services

Compensation

• Competitive base salary
• Uncapped commission with strong earning potential
• Performance-based bonuses tied to growth

Role Summary

Own and grow the Texas & South-Central territory by selling technical workforce solutions into data center construction projects. This is a field-based, hunter role focused on building a pipeline from scratch, developing relationships with key contractors and developers, and closing long-term, high-value service agreements.

Responsibilities

• Build, manage, and convert a pipeline of data center construction opportunities
• Develop relationships with General Contractors (GCs), Electrical Contractors (ECs), and developers
• Own the full sales cycle: prospecting, meetings, proposals, and closing
• Track active projects, bid cycles, and procurement timelines in the region
• Represent the company at job sites, client meetings, and industry events
• Partner with internal delivery teams to position and win work

Requirements

• 5–10+ years in sales or business development roles
• Experience selling staffing, workforce solutions, telecom, or construction services
• Proven track record closing 6–7 figure deals
• Strong ability to build relationships and generate new business
• Comfortable working in a field-based role with regional travel

Territory Overview

Texas & South Central is one of the fastest-growing data center regions, including:

• Dallas–Fort Worth (major hyperscaler cluster)
• Abilene, San Antonio, South Dallas
• Regional coverage: Louisiana, Arkansas, Mississippi, Oklahoma

DFW is the hub — most projects are drivable with occasional regional travel.

Nice to Have

• Existing relationships with contractors or developers in Texas
• Experience selling into data center or large infrastructure projects
• Familiarity with fiber, structured cabling, or low-voltage services

Compensation

• Competitive base salary
• Uncapped commission with strong earning potential
• Performance-based bonuses tied to growth