Data Architect / Delivery Lead

PamTen
United States
3 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
10 years minimum
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Microsoft Azure Computer Programming Data Architecture Information Engineering Data Governance Extract Transform Load (ETL) DevOps Python (Programming Language) Key Management Microsoft SQL Server SQL Azure
+13 more
Software Architecture Systems Development Life Cycle Azure Data Lake SQL Databases Data Streaming Data Processing Azure Data Factory Snowflake Data Lakes Pyspark Azure Synapse Analytics Data Pipelines Databricks

Job description

As a Data Architect / Delivery Lead you will design, build, and manage Azure-based data engineering solutions for healthcare clients, while leading end-to-end delivery, ensuring data quality, governance, and alignment with business requirements.

Requirements

  • 10-15+ years of experience in Data Engineering (hands-on delivery roles)
  • Experience in Healthcare Payer / Medicaid domain with knowledge of Claims, Member, Provider, Eligibility data and HIPAA/PHI compliance
  • Strong Azure-first cloud experience (architecture, implementation, operations)
  • Hands-on expertise in Azure services: ADLS Gen2, Azure Databricks, Azure Data Factory (ADF), Azure Synapse, Azure SQL / SQL Server, Azure Key Vault, Azure DevOps (Repos, Pipelines, Boards)
  • Strong programming skills in SQL (advanced querying, optimization), Python, PySpark
  • Experience building ETL / ELT pipelines and working with batch & streaming data processing
  • Strong data modeling experience: conceptual, logical, physical, dimensional (star/snowflake), SCDs, historized data
  • Knowledge of Data Lake / Lakehouse architectures, CDC, and incremental data processing
  • Ability to review and optimize data models, transformations, and schema evolution
  • Experience implementing data governance across Dev/Test/Prod environments
  • Exposure to CI/CD pipelines and DevOps practices
  • Experience with AI-assisted / Agentic development, including task breakdown, architecture definition, guardrails, and governance
  • Understanding of AI-assisted SDLC, security, compliance, auditability, and traceability.

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