Position Overview
We are seeking a highly capable Data Engineer to design, build, and optimize the data infrastructure and pipelines that power our analytics, data science, and business intelligence solutions. In this role, you will be responsible for transforming raw data into high-quality, reliable data assets for both public and private sector clients.
This position scales in technical complexity, architectural ownership, and leadership expectations based on experience. Mid and Senior-level engineers will architect enterprise data lakes, design distributed computing frameworks, manage automation workflows, and act as primary technical advisors to high-level commercial and Government leaders (Division/Branch Heads and FM&C leadership).
Key Responsibilities
- Pipeline Development & ETL/ELT: Design, construct, and maintain scalable, robust batch and real-time data ingestion pipelines from structured and unstructured sources.
- Data Architecture & Warehousing: Architect, implement, and optimize modern data warehouses, data lakes, and lakehouse environments (Databricks, Postgres).
- Data Orchestration & Automation: Build and schedule automated data workflows using orchestration engines to ensure reliable delivery and dependency management.
- Performance Tuning & Optimization: Troubleshoot, optimize, and tune complex SQL queries, database indexing, partitioned data storage, and data processing clusters.
- Data Quality & Governance: Implement automated data validation checks, schema enforcement, data lineage tracking, and strict metadata management processes.
- Systems Development Integration: Support the full Systems Development Life Cycle (SDLC) by collaborating with software engineers, systems architects, and data scientists to embed analytical solutions into production.
- Technical Guidance & Leadership: (Senior Tier) Mentor junior engineers, establish data engineering best practices, and lead the architecture of large-scale, enterprise-wide data initiatives.
Education & Certification Requirements
- Required: Bachelor’s degree from an accredited U.S. college or university in Computer Science, Data Engineering, Information Systems, Software Engineering, or a related technical discipline.
- Preferred Certifications: AWS Certified Data Engineer, Azure Data Engineer Associate, Google Professional Data Engineer, or Databricks Certified Data Engineer.
Required Experience & Qualifications
Experience requirements for this role scale progressively based on the hiring tier:
- Mid-Level: Minimum of three to five (3–5) years of progressive data engineering experience, with a proven ability to independently design and deploy stable production pipelines.
- Senior-Level: Minimum of six to nine (6–9+) years of progressive data engineering and architecture experience, including at least three (3) years leading cloud or distributed data infrastructure projects.
- Enterprise Environment: Demonstrated experience supporting Federal/State Government agencies or large enterprises (1,000+ employees).
- Core Skills: Advanced proficiency in writing clean, optimized SQL, deep understanding of relational/non-relational database design, and exceptional troubleshooting abilities.
Preferred Technical Qualifications
- Programming & API Stack: Strong development experience in Python, including framework proficiency with FastAPI, Pydantic, and Uvicorn for building robust data backends and API interfaces.
- DoD Data Ecosystem: Hands-on experience developing data pipelines, workspace enclaves, or dashboards within the DoD Advana platform, including familiarity with its core analytics stack (Databricks, Spark, Tableau, or Qlik).
- Cloud & Orchestration Stack: Hands-on experience with major cloud platforms (AWS, Azure, GCP) and data orchestration engines (Apache Airflow, Prefect, Dagster).
- Modern Data Stack Tools: Familiarity with tools like Databricks, or Docker containerization.
- Methodologies & Security: Working knowledge of Agile/Scrum, DevOps/DataOps principles, and federal cybersecurity/data privacy standards.