Data Quality & Test Automation Engineer (Senior) at Careers Decisionpointcorp, Chicago, IL
Data Quality & Test Automation Engineer (Senior) at Careers Decisionpointcorp in Chicago, IL. Full-time. Est. up to $130k. Apply with swipeandhired.
Data Quality & Test Automation Engineer (Senior)
ID
2026-4318
Job Locations
US-IL-Chicago
Category
Information Technology
Type
Regular Full-Time
Overview
DecisionPoint seeks a Senior Data Quality & Test Automation Engineer to independently validate data pipelines, schemas, calculations, datasets, and reporting outputs supporting the USMEPCOM ENLIST program’s AI integration and data analysis activities.
This position will develop SQL reconciliation tests, automated regression checks, access tests, and data freshness and performance tests. The engineer will verify that data remains accurate, complete, consistent, appropriately accessible, and traceable as it moves from source systems through transformations, warehouses, analytical models, dashboards, and reports.
The Data Quality & Test Automation Engineer will work with data engineers, AI/ML engineers, business analysts, developers, cybersecurity personnel, and Government stakeholders to identify defects, document evidence, verify corrections, and provide independent assessments of data and reporting quality.
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 Data Quality & Test Automation Engineer will:
- Independently validate data pipelines, schemas, transformation logic, calculations, datasets, and reporting outputs against documented requirements and business rules.
- Develop SQL reconciliation tests comparing source data with intermediate datasets, warehouse tables, analytical outputs, dashboards, and reports.
- Verify record counts, totals, aggregations, joins, mappings, calculated fields, and source-to-target completeness.
- Test data schemas, field types, required values, relationships, referential integrity, and handling of schema changes.
- Build automated regression checks to detect unintended changes following pipeline, schema, calculation, application, or reporting updates.
- Validate data-quality controls for accuracy, completeness, consistency, uniqueness, validity, and timeliness.
- Develop access tests to verify authorized access, role restrictions, data visibility, and expected behavior for unauthorized users.
- Test data freshness, refresh schedules, processing latency, pipeline execution times, and reporting performance.
- Validate error handling for missing, duplicate, malformed, late-arriving, or otherwise invalid data.
- Maintain repeatable test datasets, expected results, reconciliation rules, and automated dataset checks.
- Verify that training, validation, testing, and analytical datasets meet their documented quality and preparation requirements.
- Maintain traceability among requirements, business rules, test cases, test data, execution results, defects, and retest evidence.
- Integrate automated data-quality and regression checks into approved delivery pipelines and scheduled validation processes.
- Document test procedures, execut