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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

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.

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.

About Bold Business

Bold Business is an AI-first U.S.-based company building automation systems, AI agents, and production software for clients like AT&T, JP Morgan, and Verizon. We ship real systems that deliver impact — not experiments.

The Role

We’re hiring a Senior Python Engineer who can own both backend systems and the data layer behind them. This role sits between backend and data — you will build APIs, design pipelines, and ensure the data powering AI systems is clean and reliable.

You’ll work under a Lead Architect. You are expected to execute independently and own outcomes.

What You’ll Do

• Build and maintain production Python APIs (FastAPI or similar)
• Design and run data pipelines (ETL, ingestion, transformation)
• Own data quality — validation, monitoring, failure handling
• Structure data for RAG systems and AI agents
• Design and manage databases (PostgreSQL + vector DBs)
• Integrate external systems (APIs, webhooks, third-party tools)
• Debug across backend, data pipelines, and AI outputs

Non-Negotiables (Knockout Requirements)

You should not apply if you cannot clearly demonstrate all of the following:

1. Strong English communication (written and spoken)
   You can clearly explain technical concepts to non-technical stakeholders and operate in a U.S.-based team environment.
2. You have built and maintained production Python APIs
   (not tutorials or small scripts — real systems in use)
3. You have owned data pipelines end-to-end
   (ingestion → transformation → reliability in production)
4. You are strong in SQL and database design
   (schemas, performance, tradeoffs — not just basic queries)
5. You have worked with real, messy data
   (multiple sources, failures, edge cases — not clean datasets)

Required Skills

• Python (4–6 years, production backend + data work)
• FastAPI or similar frameworks
• Strong SQL and database design
• API integrations (REST, webhooks)
• Testing and clean Git workflows

AI / Data Context (Expected Working Level)

• LLM API integration (OpenAI, Anthropic, etc.)
• Understanding of how RAG systems use data
• Prompt structure and output handling
• Exposure to vector databases (pgvector, Pinecone, etc.)

Nice to Have

• LangChain, LangGraph, CrewAI
• Airflow, Prefect, or pipeline orchestration
• AWS / GCP experience
• Docker / CI-CD
• Basic BI / analytics tools

What This Role Is

• A hands-on builder role
• Ownership of backend + data layer
• High accountability, low hand-holding

What This Role Is Not

• Not a pure Data Engineer
• Not an ML research role
• Not a junior position

Final Note

• We will go deep into your past work.
• If you can clearly explain what you built, why you built it that way, and what broke — you’ll do well here.
• If not, this role will be a stretch.

Bold Business — AI-First. Delivery-First.

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.

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.

Job Summary:

We're building the blueprint for modern go-to-market at Occupier. We are seeking our first dedicated GTM Engineer—an individual who will own the connective tissue across Sales, Marketing, Customer Experience (CX), and Product. Currently, these functions operate with disparate systems and incomplete orchestration—each is well-run, but they are not wired together in ways that allow us to reach our full potential. Your mission will be to architect an AI-first revenue supply chain where every decision point—from account identification to deal closure to expansion—is informed by intelligence, optimized by automation, and freed from busywork.

Key Responsibilities:

• Design and operate account identification workflows that source, score, and rank prospects by fit and readiness at scale.
• Build multi-signal company scoring models that weight firmographic data, intent signals, product usage (for existing customers), and behavioral indicators.
• Implement contact-level scoring that identifies key decision-makers, influencers, and blockers within target accounts.
• Create feedback loops where rep activity and deal outcomes continuously improve scoring accuracy and account prioritization.
• Own the waterfall enrichment pipelines across data providers (Apollo, ZoomInfo, LinkedIn) to ensure coverage and accuracy.

Required Qualifications:

• 2–5 years of hands-on experience in GTM Engineering, RevOps, Sales Operations, or a closely related technical role at a B2B SaaS company.
• Expert-level HubSpot skills: you've built complex workflows, designed data models, and solved real operational problems.
• Deep Clay experience: you've built multi-step enrichment workflows and understand how to operationalize data pipelines at scale.
• Comfortable writing code or scripts (Python, JavaScript, or SQL) to automate and integrate.
• Data-first mindset: you think in unit economics, CAC, conversion rates, velocity, and attribution before you think in features or tools.

Benefits:

• Competitive compensation in the range of $110,000 - $120,000 CAD.
• Equity opportunities and comprehensive benefits.
• Flexible hybrid work environment in Toronto, fostering both deep focus and collaborative efforts.
• Culture that values intellectual curiosity, edge-finding, and results-driven work.

Why Occupier:

As the first GTM Engineer, you will have greenfield ownership of the entire revenue supply chain. You will collaborate with a Chief Revenue Officer who is a strategic partner in designing the architecture. Your work will ship fast and have immediate impact in a growth-stage company with startup velocity. Join us to create a blueprint for modern go-to-market and be part of a culture that emphasizes results over politics.

Role

We are seeking a highly skilled Certified DWDM Engineer to join our optical transport team. In this role, you will be responsible for the end-to-end lifecycle—design, deployment, and optimization—of our high-capacity Dense Wavelength Division Multiplexing (DWDM) networks.

As a "Certified" specialist, you are expected to bring verified expertise in industry-leading platforms (such as Ciena, Nokia, or Cisco) to ensure our fiber-optic backbone operates with maximum spectral efficiency and 99.999% reliability.

Key Responsibilities

1. Network Design & Planning

• Optical Path Engineering: Conduct link budget analysis, OSNR (Optical Signal-to-Noise Ratio) calculations, and chromatic dispersion compensation planning.
• Capacity Management: Design wavelength allocation maps and manage the growth of C-band and L-band spectrum to meet increasing data demands.
• Architecture Design: Develop resilient network topologies, including ring protection (SNCP), mesh restoration, and ROADM (Reconfigurable Optical Add-Drop Multiplexer) configurations.

1. Implementation & Integration

• Commissioning: Lead the physical installation, power-up, and software configuration of DWDM shelves, transponders, amplifiers (EDFAs/Raman), and multiplexers.
• Vendor Coordination: Work closely with hardware vendors to integrate new-generation coherent optics (e.g., 400G/800G+ wavelengths) into existing legacy fiber plants.
• Circuit Provisioning: Manage the end-to-end provisioning of high-bandwidth services (Ethernet, OTN, Fibre Channel) across the optical transport layer.

1. Maintenance & Troubleshooting

• Advanced Diagnostics: Utilize OTDR (Optical Time-Domain Reflectometer), OSA (Optical Spectrum Analyzer), and BERT (Bit Error Rate Test) equipment to localize fiber faults and signal degradation.
• NOC Support: Act as the Tier 3 escalation point for complex optical layer incidents, performing deep-dive root cause analysis on "flapping" links or high Pre-FEC BER issues.
• Performance Tuning: Monitor and optimize optical power levels and gain tilt across long-haul spans to maintain signal integrity.

Required Skills & Experience

• Bachelor’s degree in Electrical Engineering, Telecommunications, or a related technical field.
• Deep understanding of ITU-T G.709 (OTN) standards, WDM physics, and fiber types (G.652, G.655).
• Proficiency with Network Management Systems (NMS) and SDN controllers for optical automation.
• Experience using optical modeling software (e.g., Ciena OnePlanner, Nokia 1830 EPT, or VPIphotonics).

Professional Certifications (Preferred)

• Ciena: Optical Communications Professional (OC-P) or Consultant (OC-C).
• Nokia: Certified Optical Network Professional (ONP).
• Cisco: CCNP Optical or specialized Cisco Transport certificates.
• General: OTT Certified Optical Network Engineer (CONE).

The Ideal Candidate

You are a "fiber-first" engineer who thrives on the physics of light. You understand that a single fiber break can impact terabits of data, and you possess the meticulous attention to detail required to manage the invisible infrastructure that powers the modern internet.

Industry Insight 2026: Preference will be given to candidates with experience in 400ZR/ZR+ pluggable optics and SDN-based optical grooming.

As a Solutions Engineer at Intelligems, you’ll own the technical relationship with our customers from the first conversation through expansion. You'll work alongside Sales to remove technical barriers that slow or kill deals, alongside Onboarding to ensure what we sell is what we implement, and alongside Customer Success to identify and unlock expansion opportunities that require new solutioning. The thread connecting all of it is you: the person who understands both what Intelligems can do technically and what the customer is actually trying to accomplish commercially.

Intelligems operates in a technically complex environment — Shopify storefronts, headless architectures, custom integrations, dynamic pricing logic — and the gap between a customer understanding what's possible and a customer believing it's possible for them is real. You're the person who closes that gap.

Key Responsibilities

Own the technical pre-sale motion

• Partner with Account Executives on deals that have technical complexity: integration scoping, custom or headless architecture, and anything where a wrong answer in the first conversation costs us the deal
• Lead technical discovery: understand how a customer's stack is built, where Intelligems fits, and what the implementation will actually require
• Remove technical objections before they become blockers. Turn "I'm not sure if this works with our setup" into a clear path to launch

Drive technical expansion post-sale

• Support Onboarding’s adoption goals by removing technical blockers as customers are learning our platform
• Partner with Customer Success to identify accounts where new solutioning unlocks new revenue: new test types, re-integration scoping, headless implementations, and more
• Translate what the customer wants to accomplish into a concrete technical path forward, and help CS close the deal

What You Bring

• Commercial instincts: you understand that your job is to help the company grow, not just to answer technical questions. You think about deals, velocity, and retention — and you're motivated by those outcomes. You're comfortable being measured on revenue, not just resolution time
• Technical depth: you can hold your own in a conversation about API integrations, JavaScript implementations, Shopify architecture, and custom data pipelines. You don't need to be an engineer, but you need to understand what engineers are building well enough to scope it accurately and explain it clearly
• Customer presence: you're confident in customer-facing conversations, including sales calls where the stakes are real and the customer hasn't committed yet. You know how to build trust quickly, set accurate expectations, and handle pushback without losing the room

Qualifications

• 3+ years in a Solutions Engineering, Sales Engineering, or Technical Account Management role, or a customer-facing technical role where you were measured on commercial outcomes
• Experience in B2B SaaS, ideally in a product that required hands-on implementation or integration work
• Comfortable working across a full customer lifecycle and partnering with Sales & Customer Success teams
• Familiarity with e-commerce platforms, particularly Shopify
• Nice to haves:
• • Experience with A/B testing, pricing tools, or personalization platforms
  • Familiarity with headless commerce architectures or custom Shopify implementations
  • Experience in a startup or high-growth environment where the playbook wasn't written yet
  • Strong preference for Pacific or Mountain time zone