Senior Azure Databricks Data Engineer

• Assists with the data technology strategy, including resolvingstrategic data integration issues, such as data quality and stewardship,real-time,
• Event-based data integration, and crafting a universal vision for data integration
• Provides direction and oversight for the design and implementation of data solutions for integration and synthesis across multiple secure systems
• Assists with the definition, implementation, and continued adherence of new data management policies and design standards for data engineering
• Provides advice, training, and technical support for various projects
• Participates in project activities such as requirements gathering, solution reviews, and explaining technical complexities to non-technical personnel
• Work closely with the solution leads, project managers, data architects, data scientists, and data analysts on solution design, architecture, and implementation
• Assists with extraction, transformation, and loading of data from a wide variety of data sources using various data engineering tools and methods.
• Designs, builds, and maintains batch and streaming data pipelines in Azure Databricks using Py Spark, Spark SQL, and Delta Lake, following a medallion (bronze/silver/gold) lake house architecture
• Develops, loads, and tunes dimensional data models and SQL workloads in Azure Synapse Analytics (dedicated and serverless SQL pools) to support reporting and dashboard needs
• Orchestrates and monitors data workflows using Databricks Workflows/Jobs and Synapse or Azure Data Factory pipelines, including scheduling, alerting, and failure recovery
• Applies data governance and security controls (e.g., Unity Catalog permissions, role-based access, row- and column-level security, data masking) to protect sensitive health data (PHI/PII) in accordance with HIPAA and agency policy
• Assists with documenting and demonstrating solutions by developing documentation, flowcharts, layouts, diagrams, charts, code comments and clear code.
• Assists in operations by conducting systems analysis; recommendingchanges in policies and procedures.
• The job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee. Other duties, responsibilities and activities may change or be assigned at any time., Relevant certifications and coursework may substitute for formal education/experience requirements Required 4 Developing with Databricks notebooks in PySpark, Spark SQL, and R, including transformations, joins, aggregations, and window functions Required 4 ACID transactions, MERGE upserts, schema enforcement/evolution, time travel, and table maintenance (OPTIMIZE, Z-order, VACUUM) Required 4 Working with medallion (bronze/silver/gold) architecture and incremental ingestion via Auto Loader, Structured Streaming, or Lakeflow/DLT Required 4 Using Unity Catalog for Workspace Catalogs, schemas, permissions, lineage, external locations, and storage credentials Required 4 Right-sizing clusters and SQL warehouses and diagnose partitioning, shuffle, skew, and caching issues via Spark UI Required 4 Scheduling and managing dependencies with ADF Pipelines, Databricks Workflows (Jobs), and using Git folders (Repos) for source control Required 4 Designing MPP warehouse tables, including distribution (hash, round-robin, replicated), columnstore indexes, and partitioning Required 4 Querying Parquet, Delta, and CSV files in ADLS Gen2 using OPENROWSET, external tables, and views Required 4 Building star-schema facts, dimensions, stored procedures, and views, including SCD Type 1 and Type 2 Required 4 Developing with COPY INTO, PolyBase, and CETAS, and building parameterized Synapse/ADF pipelines (copy, data flows, triggers) Required 4 Workload management, resource classes, statistics, materialized views, result-set caching, and pool scaling/pausing Required 4 ADLS Gen2 folder/file-format design (Parquet/Delta), Entra ID access control, managed identities, and Key Vault Required 4 Git and CI/CD in Azure DevOps or GitHub, including branching, PRs, and deploying Synapse artifacts and Databricks Asset Bundles Required 4 Monitoring pipelines and workloads with Azure Monitor/Log Analytics and alerting, and troubleshooting failed jobs Required 4 Strong written and verbal communication skills Required Using Microsoft lakehouse patterns, including interoperability and migration paths between Synapse and Databricks Required 4 Databricks Certified Data Engineer Associate (Databricks Certified Data Engineer Professional a plus) Highly desired Microsoft Certified: Azure Data Engineer Associate (DP-203) if already held Highly desired Power BI connectivity to Synapse (Import vs. DirectQuery) and how model design affects dashboard performance Desired 4

Actual Years of Experience Master’s degree in data science, analytics, informatics, computer science, mathematics, engineering, or related field; OR…. Required 4 Bachelor’s degree in a related field plus 2+ years of relevant experience; or 4+ years of hands-on experience in a similar data engineering role; OR..