Healthcare Data Engineer - Eligibility & Integrations (Microsoft Fabric)

We are seeking an experienced Data Engineer with a strong focus on eligibility and membership data to join our Data Engineering team. This role is responsible for designing, building, and supporting scalable data pipelines and analytics solutions within a Microsoft Fabric workspace, with a primary emphasis on eligibility ingestion, validation, exclusion logic, and downstream integrations.

The ideal candidate has hands-on experience working with healthcare eligibility data, understands contract/PBP and carve-out complexity, and is comfortable operating across ingestion, transformation, and analytics layers. This role partners closely with product, integration, analytics, and operations teams to ensure accurate, compliant, and reliable eligibility data delivery.

Key Responsibilities

Eligibility & Data Engineering

• Design, build, and maintain eligibility and membership data pipelines using Microsoft Fabric.

• Ensure eligibility data is accurately prepared for downstream integrations and analytics use cases.

• Perform root cause analysis on eligibility issues, ingestion failures, and integration discrepancies.

Microsoft Fabric & Analytics Platform

• Build and maintain data solutions using Fabric Lakehouse and Warehouse patterns.

• Develop reliable ETL / ELT pipelines using Fabric Data Pipelines, Dataflows Gen2, SQL, and Python.

• Use Fabric Notebooks (Python / PySpark) for data transformation, validation, and ad-hoc analysis.

• Create and manage conformed fact and dimension tables for eligibility and operational reporting.

• Optimize performance, reliability, and observability of pipelines within the Fabric workspace.

Data Quality, Governance & Compliance

• Implement data validation and quality checks to prevent invalid or incomplete eligibility data from propagating downstream.

• Support HIPAA-aligned data handling practices related to eligibility and member data.

• Ensure secure, governed data access across environments and consumer teams.

• Document eligibility rules, mappings, and transformations clearly for audit and operational transparency.

Collaboration & Enablement

• Work with Product, Integration, Operations, and Analytics teams to support eligibility use cases and troubleshooting.

• Enable self-service reporting and analytics through Power BI semantic models.

• Partner with other data engineers to standardize patterns and improve platform consistency.

Required Qualifications

• 5+ years of experience in a Data Engineer or similar role.

• Strong hands-on experience with SQL (advanced querying, transformations, performance tuning).

• Experience building and supporting data pipelines and data models for eligibility or healthcare datasets.

• Experience working in Microsoft Fabric or closely related Azure data platforms.

• Strong understanding of data integration concepts, ingestion pipelines, and downstream system dependencies.

• Experience performing data analysis and troubleshooting across complex data flows.

Preferred Experience & Tools

Microsoft Fabric:

• Lakehouse

• Warehouse

• Data Pipelines

• Dataflows Gen2

• OneLake

Azure & Data Technologies:

• Azure SQL / SQL Server

• Azure Fabric

• Azure Synapse (dedicated pools)

• Azure Data Factory (or equivalent ETL tools)

Analytics & Visualization:

• Power BI (semantic models, measures, data modeling)

Healthcare Data (Preferred):

• Eligibility and membership data

• Contract / PBP configuration

• Carve-outs and exclusion logic

• Experience with CI/CD for data pipelines and environment promotion

tango provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. tango will make reasonable accommodations for qualified individuals with known disabilities unless doing so would result in an undue hardship.