Staff - Data Quality & Test Engineer, Development (51576)

Citrin Cooperman offers a dynamic work environment, fostering professional growth and collaboration. We’re continuously seeking talented individuals who bring a problem-solving mindset, fresh perspectives, and sharp technical expertise. We know you have choices, so our team of collaborative, innovative professionals are ready to support your professional development. At Citrin Cooperman, we offer competitive compensation and benefits and most importantly, the flexibility to manage your personal and professional life to focus on what matters most to you!

We are seeking a Staff – Data Quality & Test Engineer, Development, to join our Development team within the Information Technology department. We’re establishing a Data Operations team to build and scale our foundational enterprise data platform using Microsoft Fabric. You’ll own the validation frameworks, testing lifecycle, and overall data integrity of our core analytics ecosystem.

In this role, you’ll be the primary defender of data trust. As we migrate from legacy on-premises SQL environments to a modern Fabric data estate, your mandate is to ensure absolute accuracy throughout the transition. You’ll design, automate, and execute testing strategies across complex ETL/ELT pipelines, as well as validate the transition of data through our Medallion architecture (Bronze, Silver, Gold layers). Working closely with Data Engineers and DBAs, you’ll shift our QA processes “left,” embedding automated data quality checks directly into our CI/CD deployment pipelines. The ideal candidate is a deeply analytical skeptic who loves breaking code in lower environments to ensure it never fails in production.

Responsibilities are, but not limited to

• Source-to-Target Validation: Design and execute rigorous migration testing. Ensure data extracted from legacy on-premises SQL Servers and SaaS providers matches the data loaded into Fabric OneLake with 100% fidelity, accounting for structural transformations.
• Automated Data Quality (ADQ): Develop and implement automated testing frameworks (e.g., using Python, PySpark, or tools like Great Expectations/dbt tests) to continuously check for data freshness, completeness, uniqueness, and referential integrity.
• Pipeline & ETL QA: Test the logic of Azure Data Factory pipelines and Fabric notebooks. Validate that business rules and transformations are accurately applied before data reaches end-user reporting layers.
• CI/CD Test Integration: Embed automated test scripts into Azure DevOps/GitHub Actions pipelines so that no new data model or pipeline code can be deployed to production without passing rigorous regression tests.
• Performance & Load Testing: Partner with DBAs and Cloud Engineers to simulate heavy query loads, ensuring the Fabric capacities and SQL endpoints perform within strict enterprise SLAs.
• Incident Triage & Alerting: Configure alerting thresholds for data anomalies. When a data pipeline runs successfully but generates bad data (e.g., an unexpected spike in null values), ensure the team is alerted immediately before stakeholders see the report.