AI Data Quality Assurance Engineer

Fitch; New York, Northern (NY, KY); Hybrid; USD 115,000 - 130,000; Full time

Job summary

Fitch seeks an AI Data Quality Assurance Engineer to validate data pipelines, large-scale datasets, and ML inference outputs within enterprise-scale data platforms. You will define data quality strategies, build automation, and collaborate with data engineers, analytics teams, and product owners to ensure end-to-end data correctness.

The role focuses on schema validation, data completeness, lineage, reconciliation, and performance across data delivery lifecycles, including validation for

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, or 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 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.
• Exposure to ML inference outputs or AI-enabled consumers, focusing on validating data inputs/outputs.
• Familiarity with regulated or data-sensitive environments, including auditability and traceability requirements.

Responsibilities

• Define and implement data quality strategies for data-driven platforms, including pipelines and downstream consumption layers.
• Establish data quality gates covering accuracy, completeness, consistency, timeliness, and reliability.
• Validate data behavior against business rules and documented data contracts.
• Design and execute tests for batch and streaming data pipelines to ensure end-to-end correctness.
• Validate data transformations, aggregations, and reconciliations across sources and consumers.
• Ensure analytics, reporting, and ML outputs are accurate, consistent, and reproducible.
• Validate data feeding AI-enabled systems focusing on inputs/outputs and impact on core datasets.
• Collaborate with Data Engineers, Analytics teams, Software Engineers, and Product Owners.

Skills

QA

Python

CI/CD

Data quality testing

Data pipelines

AI data validation

Education

Bachelor’s degree in CS/related field

Job description

AI Data Quality Assurance Engineer

Requisition ID: 49889

Location: New York, NY, US

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

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
• 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 frameworks used for data, service, or platform validation
• Awareness of AI‑enabled systems (e.g., LLMs or agentic workflows) where they consume or produce data is a plus, but not required

Why Fitch?

At Fitch Group, the combined power of our global perspectives is what differentiates us. Our global network of colleagues comes together to accomplish things greater than they ever could alone.

Every team member is essential to our business and each perspective is critical to our success. We embrace a diverse culture that encourages a free exchange of ideas, guaranteeing your voice will be heard and your work will have an impact, regardless of seniority.

We are building incredible things at Fitch, and we invite you to join us on our journey.

Fitch Group is a global leader in financial information services with operations in more than 30 countries. Wholly owned by the Hearst Corporation, we are comprised of three main businesses: Fitch Ratings | Fitch Solutions | Fitch Learning.

For more information please visit our websites:

Fitch is committed to providing global securities markets with objective, timely, independent and forward-looking credit opinions. To protect Fitch’s credibility and reputation, our employees must take every precaution to avoid conflicts of interests or any appearance of a conflict of interest. Should you be successful in the recruitment process at Fitch Ratings you will be asked to declare any securities holdings and other potential conflicts prior to commencing employment. If you, or your immediate family, have any holdings that may conflict with your work responsibilities, you may be asked to divest yourself of them before beginning work.

Fitch Group is proud to be an Equal Opportunity and Affinitive Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.

FOR NEW YORK ROLES ONLY: Expected base pay rates for the role will be between $115,000 and $130,000 per year. Actual salaries will be determined on an individualized basis and may vary based on factors including but not limited to education, training, experience, past performance, and other job‑related factors. Base pay is one part of Fitch’s total compensation package, which, depending on the position, may also include commission earnings, discretionary bonuses, long‑term incentives, and other benefits sponsored by Fitch.