Technical Lead – Fabric Data Engineering

Experience: 8–12 years

Role Summary:

We are looking for a Technical Lead with deep Microsoft Fabric expertise to drive the hands-on engineering execution of a production claims data platform for a specialty medical stop loss insurance enterprise. You will own the build of Fabric Data Factory pipelines, Bronze-to-Silver-to-Gold transformations, and the data quality control layer — leading a team of Senior Data Engineers through a compressed, high-stakes delivery. This role sits at the intersection of architecture and engineering: you translate specs into working pipelines, enforce standards across the team, and personally own the toughest workstreams.

Why This Role Matters

The customer’s claims intake process — spanning 250+ Third-Party Administrators and 50,000+ claims per year — currently depends on manual reconciliation of inconsistent file formats. Every pipeline you build directly eliminates that bottleneck. The platform must reach production by a hard deadline before the annual claims season peak. The quality of your engineering in Phase 1 determines whether the customer can scale this platform to 250+ TPAs in subsequent phases — or has to redo it.

What You’ll Do

Build and maintain Fabric Data Factory pipelines for batch file intake from five Phase 1 TPAs — handling xlsx, csv, and txt files delivered via email, FTP, and secure portal

Implement the AI-assisted schema registry: alias-based versioning, schema variant mapping (~10 variants per TPA), and drift detection to catch format changes before they break ingestion

Build file receipt and SLA monitoring: control totals and balancing checks, duplicate file and claim detection, quarantine routing, and rejection logging with structured reason codes

Integrate Azure Document Intelligence into the eligibility intake pipeline — configure extraction models for PDF and screenshot eligibility documents, implement validation thresholds, and build the handoff to human-in-the-loop review

Tech Stack Snapshot

Microsoft Fabric (OneLake, Data Factory, Lakehouses, Delta Lake, Notebooks), Azure Document Intelligence, Microsoft Purview, Python, SQL, WinAIDM (Ingestion / Quality / Transformation Agents), Azure AI Services, Azure DevOps, Git.

Must-Have Skills

8+ years in data engineering; 2+ years as a Technical Lead or senior IC owning pipeline design and full team output

Production-grade hands-on experience in Microsoft Fabric — Fabric Data Factory, OneLake Lakehouses, Delta Lake patterns, and medallion architecture implementation end-to-end

Expert Python and SQL — you write the hard transformations, review every PR, and debug production data issues under deadline

Proven experience building data quality frameworks: schema validation, control totals, reconciliation, exception triage, and quarantine/reject workflows

Experience with Azure Document Intelligence (Form Recognizer) or equivalent AI document extraction services integrated into batch data pipelines

Strong grasp of canonical data modeling for transactional domains — healthcare, insurance, or financial services domain experience valued

Track record delivering end-to-end on a data engineering workstream in an offshore/onshore collaboration model

Good to Have

Azure Databricks / PySpark experience for Spark-based transformation or large-scale historical data processing workloads

Power BI or Fabric semantic layer experience — useful for validating Gold layer views against business consumer requirements

Exposure to healthcare claims data formats (837, 835) or stop loss insurance / TPA ecosystem concepts

Microsoft Fabric Analytics Engineer (DP-600) certification

What Success Looks Like (6–12 Months)

All five TPA ingestion pipelines in production — running on schedule, handling file variants correctly, and routing exceptions to the review queue with zero manual intervention needed outside the queue

Bronze-to-Silver-to-Gold pipeline fully operational: canonical model live, MDM mastering propagating through all layers, six Gold views validated by the client’s business consumers

Historical migration completed — test-module decision documented per TPA, full reprocessing or Bronze archival executed to plan and timeline

Zero architecture divergence across the Senior DE team — consistent Fabric patterns, WinAIDM usage, and DQ standards in every workstream

QA Lead and architects consistently describe the offshore pipeline quality as production-ready with minimal rework

Why Join WinWire: Your Path to Professional Excellence in the Age of AI

As an Employer of Choice, WinWire offers an exceptional workplace culture, industry recognition, and an unwavering commitment to employee growth. Here is why top talent chooses us:

AI-First. Globally Recognized: Honored at the World Economic Forum in Davos for our pioneering "Agentic AI @ Scale" work, cementing our position as a forward-thinking, AI-first services firm shaping the future of enterprise technology. Read Press Release

Microsoft Gold Standard: A 6x Microsoft Global Partner of the Year Winner/Finalist — reflecting our consistent delivery of industry-leading solutions and our deep, trusted partnership with Microsoft. Learn more about our MSFT relationship

Joining the NTT DATA Family: WinWire is a part of NTT DATA, a $30 billion global leader in AI, digital business, and technology services. This means greater career stability, broader client exposure, and access to a vast landscape of opportunities across a truly global organization. Read Press Release

Award-Winning Culture: Certified as a Best Place to Work four times, our workplace culture is not just a talking point — it is a proven, recognized commitment to creating an environment where people genuinely thrive. Watch video

People-First Environment: With a strong 4.0 Glassdoor rating, our employees speak for themselves. We prioritize wellbeing, inclusivity, and a culture of respect at every level of the organization.

Invest in Your Growth: From continuous learning programs to structured career development pathways, we equip our team with the skills, tools, and opportunities needed to grow and lead in the age of AI.