Lead Data/ETL Engineer at Careers Decisionpointcorp, Chicago, IL
Lead Data/ETL Engineer at Careers Decisionpointcorp in Chicago, IL. Full-time. Est. up to $160k. Apply with swipeandhired.
DecisionPoint seeks a Lead Data/ETL Engineer to provide technical leadership and hands-on engineering for data ingestion, transformation, integration, and delivery under the USMEPCOM ENLIST program.
This SME-level position will maintain and optimize SQL Server Integration Services (SSIS), implement approved API integrations and incremental loading approaches, and build raw, standardized, and curated data layers. The engineer will establish reliable processes for reconciliation, retries, schema changes, identifier mappings, lineage, and failed-record handling.
The Lead Data/ETL Engineer will collaborate with database administrators, architects, governance personnel, AI/ML engineers, BI developers, testers, and Government stakeholders to deliver accurate, traceable, and maintainable data pipelines supporting mission analysis, AI capabilities, and reporting.
This position is remote, with Chicago-area candidates preferred.
Note: By applying to this position, you acknowledge and consent to having your resume included in an active competitive government contract bid.
Duties & Responsibilities
The Lead Data/ETL Engineer will:
- Lead the design, implementation, maintenance, and optimization of enterprise data integration and ETL pipelines.
- Maintain and optimize SSIS packages, configurations, schedules, dependencies, and execution processes.
- Develop SQL queries, stored procedures, transformations, and reconciliation logic.
- Use Python or another approved automation language to support ingestion, orchestration, validation, monitoring, and recovery.
- Implement approved API integrations, including authentication, pagination, rate-limit handling, retries, and error processing.
- Design incremental loading strategies and implement change-data capture where appropriate and approved.
- Build and maintain raw, standardized, and curated data layers with documented transformation rules.
- Establish source-to-target mappings, common identifier mappings, and consistent handling of reference data.
- Implement reconciliation checks to verify record counts, totals, completeness, and transformation accuracy.
- Develop restartable processing, controlled retries, checkpoints, and recovery procedures.
- Implement failed-record handling, quarantine processes, error logging, and controlled reprocessing.
- Assess and manage schema changes and their effects on pipelines, datasets, analytical models, and reports.
- Maintain data lineage, transformation documentation, interface specifications, and pipeline dependencies.
- Monitor pipeline execution, throughput, freshness, resource utilization,