NOTE: Must able to Obtain Security Clearance
Job Description:
The individual serves as the authoritative resource for the agency who specializes in preparing big data infrastructure for analytical or operational uses. They are responsible for designing and creating systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret and enables the agency to make smarter decisions and optimize operations.
Position Requirements
Primary Requirements
Lead the technical migration from legacy SQL Server stored procedures and ADF pipelines to Databricks Lakehouse (Delta Lake), ensuring best practice Lakehouse design.
Translate traditional relational data warehousing paradigms into scalable, distributed Lakehouse frameworks (Bronze, Silver, Gold).
Design robust, reusable ETL/ELT frameworks using PySpark, Delta Live Tables (DLT), and Databricks Workflows.
Architect and refine the Gold Layer (dimensional models, star schemas) specifically to maximize Power BI performance.
Optimize Databricks SQL Warehouses to support high-concurrency, low-latency Power BI queries (DirectQuery and Import modes).
Implement advanced optimization techniques, including Z-Ordering, data skipping, liquid clustering, and materialized views.
Define and enforce governance standards for cluster sizing, auto-scaling policies, and serverless SQL compute to balance performance with cost.
Implement proactive monitoring dashboards to track Databricks Unit (DBU) consumption and identify cost-saving opportunities.
Establish best practices for partition strategies and file size management within Delta Lake.
Design and implement a robust data security model using Unity Catalog for centralized governance.
Enforce row-level and column-level security policies to ensure compliant data access for Power BI consumers and internal analysts.
Align the Lakehouse security architecture with existing enterprise Azure Active Directory (Microsoft Entra ID) and RBAC standards.
Act as the primary technical lead, conducting dedicated pair-programming sessions, workshops, and code reviews to transition the team from SQL-centric to Spark-centric thinking.
Create comprehensive technical documentation, including architecture diagrams, design patterns, and optimization playbooks.
Build a foundational knowledge transfer framework to ensure the internal team is fully self-sufficient post-migration.
Communicate effectively verbally and in written form to both technical and non-technical audience
Work in an organized fashion, completing tasks timely while paying close attention to details
Desired KSAs
Mastery of data engineering principles, including data modeling, ETL (Extract, Transform, Load) processes, and data pipelines
Proficiency with Azure Data Lake data storage and processing services
Skilled at designing, building, and optimizing data pipelines for ingesting, transforming, and loading data
Proficiency in languages such as SQL and Python/PySpark for data manipulation and pipeline development
Skilled at identifying and resolving data-related challenges
Skilled at creating efficient data models that meet business requirements.
Skilled at optimizing query performance and system scalability
Experience
Must have a Bachelor's degree or higher from an accredited college or university in Computer Science, Engineering, or a related technical field
5+ years experience in data engineering, data system development or related roles
5+ years experience with cloud platforms (e.g. Azure, AWS, Google Cloud Platform)
1+ year leading complex, cross-functional data projects and technical teams
Experience with Databricks Lakehouse, Apache Spark, Delta Lake, cloud-native databases, storage solutions, and distributed compute platforms
Experience with data warehousing, dimensional modeling, enterprise data lakes, incremental data loads, and metadata-driven ingestion and data quality frameworks using PySpark
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