Senior Data Quality / QA & UAT Lead

• Employment Type: Part-Time Consultant / Technical Validation Authority
• Target Allocation: 10–14 hours/week average (Note: Workload rises substantially during active milestone acceptance gates, multi-agency onboarding validation waves, and final platform performance tuning).
• Technical Reality: This role carries direct contractual utility. The project Request for Quotes (RFQ) establishes strict, legally binding quantitative performance thresholds. Every deliverable must clear meticulous independent verification before payment is authorized.
• Compensation Type: Milestone / Deliverable-Based Fixed-Price Engagement.

• Verification Matrix Management: Build and maintain the master integrated verification matrix mapping technical test coverage across all 15 sequential project deliverables.
• Data Quality Benchmarking: Formally validate that data structures maintain a 98% or greater field completeness rating and a 95% or greater cross-system correlation accuracy rate.
• Pre-Submission Rule Auditing: Independently test and audit the enterprise business rule library to mathematically verify that analytics outputs maintain a 95% or greater rule-output accuracy rate and a 5% or lower false-positive rate before state submission.
• Pipeline Reliability Testing: Validate end-to-end data pipeline runs, ensuring automated ingestion, Slowly Changing Dimension Type 2 (SCD2) versioning, and ledger reconciliation modules perform with zero critical errors.
• UAT Orchestration: Lead, structure, and coordinate User Acceptance Testing (UAT) scripts, test cases, and onboarding execution logs alongside OCIG program staff and agency Inspector General participants.
• Forensic Evidence Compilation: Prepare, version-control, and package comprehensive test datasets, logs, scripts, and configuration details to support independent state validation teams during formal system reruns.
• Performance & Latency Tuning Verification: Rigorously test and document platform performance metrics, verifying that dashboard page loads remain under 5 seconds, query filter responses remain under 2 seconds, and total pipeline execution durations conclude within 2 hours.
• Defect Tracking & Regression: Manage the complete defect lifecycle, executing rigorous regression testing and retesting corrected bugs to ensure zero unresolved critical vulnerabilities or schema defects remain in the code base.
• Reproducibility Validation: Conduct structural operational audits on all system documentation, rule libraries, and data dictionaries to confirm they are completely reproducible by a third party outside the core engineering team.

• Experience Baseline: 8+ years of dedicated experience in software QA, data testing, data quality engineering, or technical systems validation.
• Government Context: Proven history designing testing frameworks, user validation parameters, or QA metrics within a local, state, military, or federal government IT program.
• Pipeline Vetting: Extensive experience testing enterprise ETL/ELT pipelines, validating raw-to-curated storage layers, and scripting source-to-target data reconciliations.
• Financial Data QA: Strong background executing record-count verifications, control-total testing, schema evolution validation, and profiling multi-source transactional ledger records.
• Technical Tool Stack: Advanced proficiency using SQL for data exploration and hands-on experience handling API integration testing.
• Structural Testing: Solid background in regression testing, requirements-to-test traceability mapping, and quantitative performance/stress testing.
• Environment Management: Experience writing structured test scripts and maintaining isolated, version-controlled test datasets.
• Defect Governance: Expert-level competence in structural defect management, tracking metrics, and building reproducibility evidence packages.
• Vetting & Location: Must be a U.S.-based citizen or resident. Must be able to successfully clear an FDLE Level II background screening (including fingerprinting) within 5 business days of contract award.

• Hands-on experience executing automated data quality testing and pipeline validation inside Azure Databricks or Azure Data Lake Storage Gen2 environments.
• Practical familiarity utilizing programmatic data quality frameworks such as Python/PyTest, Great Expectations, Deequ, or comparable automated testing tools.
• Experience validating enterprise Microsoft Power BI dashboards, semantic data models, and specialized reporting layout views.
• Background supporting formal Government Independent Verification and Validation (IV&V / ITV) processes or external agency financial audits.
• Working familiarity with Section 508 accessibility compliance testing standards.
• Experience validating complex analytical models, machine learning outliers, or hybrid rules-plus-ML detection architectures.

1. The Government Customer: Explicitly name the agency and the specific system testing context (e.g., Federal Administration, Military Branch, State Department Office).
2. The Specific Performance Metrics: Detail the exact quantitative contract thresholds you were responsible for verifying (e.g., "Validated 99% pipeline run success rates," "Maintained data completeness benchmarks").
3. System Scale & Complexity: Specify the exact size of the test datasets, the number of distinct user roles or legacy systems integrated, and the precise project duration.
4. Personal Contribution: Detail exactly what you personally tested, scripted, or orchestrated (e.g., "Authored the master UAT plan for 10 onboarding departments," "Coded automated data profiling regression scripts using PyTest").
5. Deployment & Acceptance Status: Explicitly state the true production, operational, or formal sign-off status achieved by deliverables under your validation oversight.
6. Quantifiable Quality Results: Include the precise metrics achieved under your guidance, such as percentage reduction in production defects, testing turnaround times, or independent audit pass rates.

“We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status”

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