Data Engineer

What You Will Do:

• • Design, build, optimize, and maintain data pipelines and integration workflows

• • Develop ETL and ELT processes to ingest, transform, validate, and load data from multiple sources

• • Support modern data platforms including data lakes, lakehouses, warehouses, marts, and related architectures

• • Help enable high-volume and high-velocity data processing for mission and operational needs

• • Partner with data scientists, data architects, and analysts to support analytics-ready datasets and reusable data assets

• • Implement data structures and data models aligned to business and technical requirements

• • Support data quality, metadata, lineage, and governance objectives through strong engineering practices

• • Contribute to cloud-based and hybrid data modernization efforts

• • Produce technical documentation and delivery artifacts required for project execution

What You Will Need:

Level and Compensation: This position may be filled at multiple career levels based on business need and the selected candidate's qualifications, relevant experience, and demonstrated capability. Typical leveling for this role is as follows:

Junior (0 to 4 years)

Mid-Level (5 to 9 years)

Senior (10 to 14 years)

Principal (15 or more years)

These ranges are guidelines only and are not the sole determining factor in level. The posted compensation range reflects the full range across all possible levels. Individual offers will be based on the level at which the candidate is hired, as well as factors such as skills, experience, internal equity, and geographic market considerations where applicable.

Education and Experience

• • Bachelor's degree in Computer Science, Engineering, Information Systems, Data Engineering, or a related technical field, or equivalent practical experience

• • Relevant professional experience in data engineering, data integration, pipeline development, or related roles; the depth and scope of experience required will vary based on the level at which this position is filled

  • Experience designing and supporting data processing workflows in professional environments preferred

  • Experience working in cloud, hybrid, or enterprise data environments preferred

Knowledge, Skills, and Abilities

• • Proficiency in Python and SQL

• • Familiarity with ETL/ELT, data integration, and pipeline development

• • Familiarity with data lakes, lakehouses, data warehouses, and modern data platform patterns

• • Exposure to large-scale or high-throughput data processing environments a plus

• • Working knowledge of schema design, data modeling, and database optimization

• • Familiarity with orchestration and transformation tools used in modern data engineering environments

• • Strong collaboration and documentation skills

  Nice to Have

• • Experience in a federal, public sector, statistical, or regulated data environment

• • Familiarity with demographic, geographic, or survey-related datasets

• • Exposure to graph-based data structures or knowledge graph use cases

• • Experience with secure cloud data platforms

Reporting Relationships:

The incumbent reports directly to the Chief Data Scientist in Global Technology or other management or executive level positions. May have individual contributor direct reports.