Manager - Data QA

Annalect is seeking a hands-on Data QA Manager to lead and elevate data quality assurance practices across our growing suite of software and data products. This is a technical leadership role embedded within our Technology teams, focused on establishing best-in-class data quality processes that enable trusted, scalable, and high-performance data solutions.

As a Data QA Manager, you will drive the design, implementation, and continuous improvement of end-to-end data quality frameworks, with a strong focus on automation, validation, and governance. You will work closely with data engineering, product, and analytics teams to ensure data integrity, accuracy, and compliance across complex data pipelines, platforms, and architectures, including Data Mesh and modern cloud-based ecosystems.

This role requires deep technical expertise in SQL, Python, data testing frameworks like Great Expectations, data orchestration tools (Airbyte, DbT, Trino, Starburst), and cloud platforms (AWS, Azure, GCP). You will lead a team of Data QA Engineers while remaining actively involved in solution design, tool selection, and hands-on QA execution.

Key Responsibilities:

• Develop and implement a comprehensive data quality strategy aligned with organizational goals and product development initiatives.
• Define and enforce data quality standards, frameworks, and best practices, including data validation, profiling, cleansing, and monitoring processes.
• Establish data quality checks and automated controls to ensure the accuracy, completeness, consistency, and timeliness of data across systems.
• Collaborate with Data Engineering, Product, and other teams to design and implement scalable data quality solutions integrated within data pipelines and platforms.
• Define and track key performance indicators (KPIs) to measure data quality and effectiveness of QA processes, enabling actionable insights for continuous improvement.
• Generate and communicate regular reports on data quality metrics, issues, and trends to stakeholders, highlighting opportunities for improvement and mitigation plans.
• Maintain comprehensive documentation of data quality processes, procedures, standards, issues, resolutions, and improvements to support organizational knowledge-sharing.
• Provide training and guidance to cross-functional teams on data quality best practices, fostering a strong data quality mindset across the organization.
• Lead, mentor, and develop a team of Data QA Analysts/Engineers, promoting a high-performance, collaborative, and innovative culture.
• Provide thought leadership and subject matter expertise on data quality, influencing technical and business stakeholders toward quality-focused solutions.
• Continuously evaluate and adopt emerging tools, technologies, and methodologies to advance data quality assurance capabilities and automation.
• Stay current with industry trends, innovations, and evolving best practices in data quality, data engineering, and analytics to ensure cutting-edge solutions.

Required Skills

• 11+ years of hands-on experience in Data Quality Assurance, Data Test Automation, Data Comparison, and Validation across large-scale datasets and platforms.
• Strong proficiency in SQL for complex data querying, data validation, and data quality investigations across relational and distributed databases.
• Deep knowledge of data structures, relational and non-relational databases, stored procedures, packages, functions, and advanced data manipulation techniques.
• Practical experience with leading data quality tools such as Great Expectations, DbT tests, and data profiling and monitoring solutions.
• Experience with data mesh and distributed data architecture principles for enabling decentralized data quality frameworks.
• Hands-on experience with modern query engines and data platforms, including Trino/Presto, Starburst, and Snowflake.
• Experience working with data integration and ETL/ELT tools such as Airbyte, AWS Glue, and DbT for managing and validating data pipelines.
• Strong working knowledge of Python and related data libraries (e.g., Pandas, NumPy, SQLAlchemy) for building data quality tests and automation scripts.