Databricks Data Engineer
About The Position
Leidos is seeking a skilled Databricks Data Engineer to design, develop, and maintain scalable data engineering solutions on the Databricks Lakehouse Platform for the National Healthcare Safety Network (NHSN) support contract. This position is critical to supporting NHSN’s modern data platform strategy by leveraging the Databricks Lakehouse Platform to build scalable, cloud-native data engineering solutions. The Databricks Data Engineer will enable efficient data ingestion, transformation, governance, and analytics while reducing operational complexity, improving data accessibility, and accelerating business insights. This role will help establish a modern enterprise data ecosystem capable of supporting advanced analytics, artificial intelligence, machine learning, and data-driven decision-making across the organization. The ideal candidate will have hands-on experience building enterprise-grade data pipelines using Apache Spark, PySpark, Delta Lake, and cloud-native technologies. This role will be responsible for developing high-performance ETL/ELT pipelines, implementing modern data architecture, optimizing distributed workloads, and enabling analytics and reporting across the organization. The successful candidate will work closely with data architects, business analysts, data scientists, and application teams to deliver reliable, secure, and scalable data solutions that support business intelligence, machine learning, and advanced analytics initiatives.
Requirements
- Bachelor's degree in Computer Science, Information Technology, Engineering, Mathematics, or a related discipline.
- 3–7+ years of experience in Data Engineering or ETL development.
- Hands-on experience with Databricks and Apache Spark.
- Strong proficiency in Python and PySpark.
- Advanced SQL development skills.
- Experience designing and developing enterprise-scale ETL/ELT pipelines.
- Experience working with Delta Lake and Lakehouse architecture.
- Strong understanding of distributed data processing concepts.
- Experience with data modeling, dimensional modeling, and data warehousing.
- Experience working with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
- Experience with Git-based source control and CI/CD deployment processes.
- Strong analytical, troubleshooting, and problem-solving skills.
Nice To Haves
- Databricks Certified Data Engineer Associate or Professional certification.
- Experience with Unity Catalog, Delta Live Tables (DLT), and Databricks Workflows.
- Experience with Structured Streaming and real-time data processing.
Responsibilities
- Design, develop, and maintain scalable ETL/ELT pipelines using Databricks and Apache Spark.
- Build data ingestion frameworks to integrate structured, semi-structured, and unstructured data from various enterprise systems.
- Develop data transformation logic using PySpark and Spark SQL.
- Design, implement, and optimize Delta Lake tables for high performance, reliability, and scalability.
- Implement Medallion Architecture (Bronze, Silver, Gold) for enterprise data processing.
- Optimize Spark jobs through partitioning, caching, file compaction, and performance tuning techniques.
- Develop and maintain data workflows using Databricks Workflows, Jobs, or orchestration tools such as Apache Airflow.
- Implement data quality, validation, reconciliation, and monitoring processes to ensure data integrity.
- Collaborate with cross-functional teams to understand business requirements and deliver high-quality data solutions.
- Develop reusable data engineering frameworks, utilities, and coding standards.
- Document data lineage, transformation logic, metadata, and technical designs.
- Participate in code reviews and implement CI/CD processes using Git and DevOps tools.
- Ensure compliance with enterprise data governance, security, and access management standards.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
