Data Engineer

Spatial Front, Inc. (SFI) is seeking a Data Engineer to support our growing modernization team. SFI was recently awarded the 2025 USA Today National Top Places to Work award and the 2025 Washington Post Top Workplaces. The ideal candidate will perform hands-on data engineering to design, modernize, optimize, and support enterprise Extract, Transform, and Load (ETL) processes and analytical data warehouse structures supporting PeopleSoft HCM and related enterprise systems.

This role is focused on the movement, transformation, quality, and organization of enterprise data for reporting and analytics. The candidate will analyze existing ETL processes, design improved data-loading approaches, develop and maintain transformation logic, and support dimensional data warehouse structures including star schemas, facts, dimensions, and data marts. The candidate will work closely with BI developers, DBAs, application teams, functional analysts, and infrastructure personnel to deliver reliable, scalable, and maintainable data pipelines for Federal Government customers.

Location

Crystal City, VA - On-Site/Hybrid

Responsibilities

• Design, develop, test, deploy, and maintain enterprise ETL and data integration processes supporting analytical data warehouses and reporting environments.
• Analyze existing ETL workflows and recommend opportunities to redesign, simplify, consolidate, optimize, or replace legacy data-processing logic.
• Develop batch and incremental data loads that extract data from operational systems, transform data according to business and technical rules, and load analytical warehouse structures.
• Develop and maintain transformation logic using SQL, PL/SQL, ETL development tools, scripting languages, or other appropriate data-engineering technologies.
• Design and support analytical data warehouse structures including fact tables, dimension tables, star schemas, snowflake schemas, data marts, and other dimensional models.
• Implement slowly changing dimensions, surrogate keys, historical tracking, aggregation, lookup, reference-data, and other common data warehouse patterns.
• Develop and maintain source-to-target mappings, transformation specifications, load rules, dependencies, and supporting technical documentation.
• Work with application and functional teams to understand source-system data structures, business rules, code values, relationships, and downstream analytical requirements.
• Build data-validation and reconciliation processes to confirm completeness, accuracy, consistency, and integrity between source systems, ETL processing, warehouse structures, and downstream reports.
• Troubleshoot failed ETL jobs, data-quality issues, transformation errors, duplicate or missing records, schema changes, and discrepancies between operational and analytical systems.
• Optimize ETL processing for performance, scalability, reliability, and maintainability, including SQL tuning, bulk-processing techniques, parallel execution, load sequencing, and efficient data movement.
• Work with DBAs to optimize database structures, indexes, partitions, statistics, materialized views, and other physical database components supporting warehouse performance.
• Develop restart, recovery, exception-handling, logging, audit, and monitoring capabilities for production ETL processes.
• Support scheduling and orchestration of ETL workflows, dependencies, batch jobs, and downstream reporting processes.
• Assess the impact of source-system changes, application upgrades, database changes, and business-rule changes on existing ETL and warehouse processes.
• Support modernization of legacy ETL technologies and data-processing patterns to more maintainable and supportable approaches.
• Participate in testing, defect resolution, data reconciliation, release, migration, and production-validation activities.
• Create and maintain technical documentation including ETL designs, source-to-target mappings, data models, job flows, transformation rules, deployment procedures, and operational support guidance.
• Collaborate with BI developers to ensure warehouse structures and data pipelines support required semantic models, reports, dashboards, and analytical use cases.
• Participate in Agile/SAFe activities including PI Planning, backlog refinement, iteration execution, demonstrations, testing, and release planning.
• Other duties as assigned.