ETL/Data Engineer

enterprise data platform on Microsoft Azure. You will own end-to-end delivery of data pipelines and data products that power analytics, regulatory reporting, operational dashboards, and emerging AI/ML use cases. You will partner closely with data architects, analytics engineers, data scientists, business stakeholders, and platform engineering teams to deliver reliable, performance, secure, and costefficient data solutions. This role is ideal for an engineer with strong hands-on depth in Azure Data Factory, Azure Synapse Analytics and/or Databricks, and modern Lakehouse patterns, who is comfortable leading migration programs (e.g., Informatica-to-ADF, on-prem warehouse-to-cloud), mentoring mid-level engineers, and shaping engineering standards across the team., Pipeline Design & Development Design and build robust, reusable, parameter-driven ingestion and transformation pipelines using Azure Data Factory, Synapse Pipelines, Data Bricks and/or Microsoft Fabric Data Factory. Implement medallion architecture (Bronze / Silver / Gold) on Azure Data Lake Storage Gen2 using Delta Lake, Parquet, and structured streaming patterns. Build performant ELT workflows that leverage pushdown to source systems (Synapse Dedicated SQL Pool, Azure SQL, Teradata) where appropriate. Develop and optimize PySpark notebooks and jobs on Azure Databricks or Synapse Spark. Data Modeling & Warehousing Design dimensional models (Kimball star/snowflake) and data vault patterns for analytics consumption. Implement Slowly Changing Dimensions (Type 1/2/3), Change Data Capture, and late-arriving data patterns. Tune distributed SQL workloads in Synapse Dedicated SQL Pool / Fabric Warehouse, including distribution keys, partitioning, and clustered column store indexes. Platform Engineering & DevOps Implement CI/CD for data pipelines using Azure DevOps (YAML pipelines, ARM/Bicep/Terraform) across Dev / SIT / UAT / Prod environments. Instrument pipelines with robust logging, auditing, and monitoring using Azure Monitor, Log Analytics, and KQL. Define and enforce coding standards, code review practices, branching strategies, and release management. Migration & Modernization Lead or contribute to legacy-to-cloud migrations - e.g., Informatica PowerCenter to Azure Data Factory, on-premises Teradata / Oracle / SQL Server to Synapse or Fabric. Perform workload assessment, capacity planning, and cost modeling for target-state architectures. production incident response for critical pipelines.

Deep hands-on expertise with Azure Data Factory: pipelines, datasets, linked services, triggers, parameterization, mapping data flows, and all three Integration Runtime types (Azure, Selfhosted, SSIS). Strong Experience in Data Bricks and PySpark. Production experience with one or more of: Azure Synapse Analytics (Dedicated and Serverless SQL Pools, Spark Pools) OR Azure Databricks (Delta Lake, Unity Catalog) OR Microsoft Fabric (Warehouse, Lakehouse, OneLake). Strong working knowledge of Azure Data Lake Storage Gen2 (hierarchical namespace, RBAC + ACLs, lifecycle management, security). Experience with Azure Key Vault, Azure AD / Entra ID (including managed identities and service principals), and private networking (VNet integration, private endpoints). Monitoring and troubleshooting with Azure Monitor, Log Analytics, and KQL. Advanced SQL - window functions, CTEs, query optimization, execution plan analysis, performance tuning. Strong Python for data engineering - pandas, PySpark, REST API integration, unit testing (pytest). Proficient in T-SQL; familiarity with Spark SQL, KQL, PowerShell, and Bash shell scripting.

Required Qualifications: Deep hands-on expertise with Azure Data Factory: pipelines, datasets, linked services, triggers, parameterization, mapping data flows, and all three Integration Runtime types (Azure, Selfhosted, SSIS). Strong Experience in Data Bricks and PySpark. Production experience with one or more of: Azure Synapse Analytics (Dedicated and Serverless SQL Pools, Spark Pools) OR Azure Databricks (Delta Lake, Unity Catalog) OR Microsoft Fabric (Warehouse, Lakehouse, OneLake). Strong working knowledge of Azure Data Lake Storage Gen2 (hierarchical namespace, RBAC + ACLs, lifecycle management, security). Experience with Azure Key Vault, Azure AD / Entra ID (including managed identities and service principals), and private networking (VNet integration, private endpoints). Monitoring and troubleshooting with Azure Monitor, Log Analytics, and KQL. Advanced SQL - window functions, CTEs, query optimization, execution plan analysis, performance tuning. Strong Python for data engineering - pandas, PySpark, REST API integration, unit testing (pytest). Proficient in T-SQL; familiarity with Spark SQL, KQL, PowerShell, and Bash shell scripting. Preferred Qualifications: 5+ years of data warehouse development experience. 5+ years of data modeling experience using ERWIN or similar tools. 2+ years of experience with Azure Data Factory and Snowflake. Medicaid Domain Knowledge is a plus