AI Data Quality Assurance Engineer

Intermediate AI Data Quality Assurance Engineer- New York office.

We are seeking a Data QA Engineer to ensure the quality, reliability, robustness, and trustworthiness of data‑driven platforms that support analytics and reporting. This includes validating data pipelines, large‑scale datasets, and inference outputs, with selective exposure to LLM‑based or agentic components where they consume or produce data.

This role goes beyond traditional UI or API testing and focuses on data‑aware quality strategies, including schema validation, data completeness, lineage, reconciliation, and performance. You will ensure that core data assets and any dependent analytics or AI components behave as expected across the full data and delivery lifecycle.

What We Offer

• Opportunity to work on enterprise‑scale data platforms supporting analytics, reporting, and downstream ML/AI use cases
• Ownership of data quality strategy, tooling, and automation across core data pipelines
• Close collaboration with Data Engineers, Software Engineers, Product Owners, and Analytics teams
• Exposure to data validation frameworks , large‑scale datasets, and selective AI‑enabled data consumers
• A mandate to define, measure, and govern data quality standards across squads and delivery teams

We’ll Count on You To Data Quality Strategy

• Define and own data quality strategies for data‑driven platforms, including pipelines, transformations, and downstream consumption layers
• Establish data quality gates covering accuracy, completeness, consistency, timeliness, and reliability
• Validate data behavior against business rules, domain expectations, and documented data contracts

Data Pipeline & Consumer Validation

• Design and execute tests for batch and streaming data pipelines , ensuring end‑to‑end data correctness
• Validate data transformations, aggregations, and reconciliations across multiple sources and consumers
• Ensure analytics, reporting, and ML inference outputs are accurate, consistent, and reproducible
• Validate data feeding LLM‑based or agentic systems, focusing on inputs, outputs, and impact on core datasets

Data Integrity & Lifecycle Validation

• Validate dataset quality across ingestion, transformation, storage, and consumption stages
• Enforce schema validation, null checks, referential integrity, and lineage tracking
• Monitor for data drift, anomalies, volume changes, and performance regressions post‑deployment

Test Automation for Data Platforms

• Build and maintain automation frameworks for data quality testing , including rule‑based and statistical checks
• Integrate data quality tests into CI/CD pipelines for continuous validation
• Leverage automation to scale coverage across large and evolving datasets , while ensuring clear, auditable results
• Automate UI and service‑level validations to ensure data is correctly surfaced, consumed, and represented across dashboards, reports, APIs, and downstream services

Cross‑Functional Collaboration

• Partner closely with Data Engineers, Analytics teams, Software Engineers, and Product Owners throughout the delivery lifecycle
• Act as the quality authority for data assets within assigned squads
• Provide clear, actionable feedback on data quality risks, gaps, and improvement opportunities

What You Need to Have

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related technical discipline or equivalent practical experience in Quality Engineering for data‑driven platforms
• Strong experience in QA or Quality Engineering , preferably focused on data platforms, analytics, or reporting systems
• Hands‑on experience validating data pipelines, transformations, and large‑scale datasets
• Proficiency in Python (or similar languages) for data validation, automation, and testing workflows
• Solid understanding of data engineering concepts , including schema management, data quality checks, reconciliation, and lineage
• Experience integrating data quality tests into CI/CD pipelines for continuous validation
• Experience validating downstream data consumers , including analytics, reporting layers, services, or APIs
• Exposure to ML inference outputs or AI‑enabled consumers , with a focus on validating data inputs and outputs rather than model internals
• Familiarity with working in regulated or data‑sensitive environments , including auditability and traceability requirements
• Experience with test automation fra…