Role Overview
We are seeking an ETL QA Data Engineer to ensure the accuracy, completeness, and reliability of data pipelines and analytics outputs across a high-volume, event-driven domain (e.g., emergency response, public safety, and healthcare operations). You will validate data ingestion and transformation logic, reconcile records between source and target systems, detect anomalies, and help ensure reporting and dashboards reflect trustworthy data.
Key Responsibilities
Data Validation & Reconciliation
Perform source-to-target reconciliation for event/transaction-style datasets across ingestion and curated layers.
Validate completeness, timeliness, and consistency of daily/near-real-time ingestion pipelines.
Identify and investigate data anomalies (missing records, spikes/drops, duplicates, out-of-range values, schema drift).
Validate correctness of KPIs and metrics used in dashboards and reports (e.g., volumes, rates, turnaround times, error trends).
Ensure referential integrity across key entities (e.g., events/encounters, units/resources, locations, agencies/sites).
Partner with engineering teams to debug discrepancies, confirm root causes, and validate fixes.
Automation & Framework Development
Build and maintain automated data validation frameworks using Python (PyTest/Unittest) and data quality libraries/tools.
Develop reusable validation assets
reconciliation templates
data quality rules (nulls, ranges, uniqueness, referential integrity)
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regression suites for critical pipelines
Integrate validation into CI/CD and orchestration workflows (e.g., validation gates per pipeline run).
Produce validation artifacts (test reports, defect summaries, data quality scorecards) for ongoing monitoring.
AI-Assisted Validation (with Human Verification)
Use AI tools (LLMs, Qodo, etc.) to accelerate
validation query generation
reconciliation logic
test case creation (including edge-case discovery)
Apply AI-assisted techniques for anomaly detection ideas, boundary scenarios, and coverage expansion.
Validate all AI-generated outputs against actual data and documented business rules before adoption.
Required Qualifications
Strong SQL skills for reconciliation and validation (joins, aggregation, window functions, profiling).
Strong hands-on Python experience for test automation and data validation (not just scripting).
Experience in ETL/ELT QA, data testing, analytics validation, or data quality engineering.
Understanding of event-driven / transaction-based systems and common pipeline failure patterns (late-arriving data, corrections, duplicates).
Experience validating large datasets and reporting layers with high reliability expectations.
Preferred Qualifications
Experience with data quality tools such as
Great Expectations (preferred)
custom Python validation frameworks
dbt tests / similar validation frameworks
Familiarity with cloud data platforms and modern data stacks (any of)
Snowflake / BigQuery / Redshift
Databricks / Spark
Airflow / ADF / Prefect
Experience with sensitive data handling practices (privacy/security considerations).
Exposure to interoperability/integration patterns (APIs, HL7/FHIR, EDI, batch feeds)—as applicable to the environment.
What Success Looks Like
Automated validation coverage for critical pipelines (freshness, completeness, reconciliation).
Early detection of anomalies and measurable reduction in downstream reporting issues.
Repeatable, documented validation rules aligned to business definitions and metrics.
Efficient collaboration with engineering teams to shorten incident triage and fix verification cycles.
