Lead QA Engineer – Data & Analytics

The ideal candidate will bring a proven track record of QA with deep expertise in data testing, data quality, and validation across enterprise data platforms. This role will lead a team of 5–6 QA engineers supporting data warehouse, ODS, and data lake initiatives, ensuring the accuracy, reliability, and integrity of high-volume, business-critical data. The ideal candidate has strong technical skills, hands-on SQL expertise, and experience implementing scalable data testing frameworks.

Note: This role is located in the New York City Metro area. It's hybrid with 4 days onsite per week; and annual compensation ~$140K.

Competencies

• 7 years of experience in data testing, data quality validation, and QA leadership.
• Expertise in high-volume transactional data and complex data ecosystems.
• Advanced SQL expertise ("SQL Ninja") with ability to write complex queries across large datasets.
• Strong experience with SQL Server, Redshift, and hands-on PostgreSQL work.
• Proficiency in Python for data validation, QA automation, or test scripting.
• Experience with structured and semi-structured data (XML, JSON).
• Familiarity with Agile methodologies and modern QA practices.
• Strong communication, documentation, analytical, and problem-solving skills.
• Experience mentoring, managing, or leading technical teams.
• Local to the NYC metro area required.
• Bachelor's degree in Computer Science or related field required.

Preferred (Optional but Valuable Add-ons)

• Experience with Airflow, DBT, Databricks, Glue, or other DataOps/ELT tools.
• Exposure to cloud data platforms (AWS, GCP, Azure).
• Experience with automated data testing frameworks.

Responsibilities

Data Quality & Validation

• Lead data validation and QA activities across data warehouse, ODS, and data lake ecosystems.
• Perform ETL, data flow, and integration testing across sources such as SQL Server, Redshift, and PostgreSQL.
• Implement and maintain data quality checks, validation rules, anomaly detection, and alerting mechanisms.
• Work with datasets spanning RDBMS, NoSQL, flat files, CSVs, XML, JSON, and copybooks.
• Validate complex transformations, lineage, and reconciliation across systems.

Leadership & Collaboration

• Lead, mentor, and develop a team of 5–6 data QA engineers.
• Drive a culture of continuous improvement, modern QA practices, and scalable testing strategies.
• Collaborate closely with data engineers, developers, product managers, and cross-functional stakeholders.
• Track and report quality metrics, defect trends, and testing progress, providing insights and process enhancements.

Technical Execution

• Work across multiple concurrent data projects and deliverables.
• Utilize schedulers, ETL/integration tools (e.g., DataPower), and automation frameworks.
• Stay current with emerging tools and technologies across the data engineering and QA landscape.
• Support web and mobile application testing as needed.