Data QA Lead - Data Engineering

HiLabs is a leading AI-powered healthcare technology company transforming the US healthcare ecosystem through advanced data quality, analytics, and automation solutions. Our platform processes billions of healthcare records, helping healthcare organizations make accurate, data-driven decisions at scale. If you are passionate about data quality, building reliable data products, and driving engineering excellence, this role is for you. Role Overview We are looking for a hands-on QA Lead / Manager, Data Engineering who can define and drive the quality strategy for our data platforms and productization initiatives. This role requires deep expertise in data testing, data quality, pipeline validation, monitoring, and observability. You will work closely with Data Engineering, Product, and Business teams to ensure the accuracy, reliability, scalability, and operational readiness of our data products. The ideal candidate will act as an independent quality owner and bring a strong "second-eye" approach to validating complex data workflows. Key Responsibilities Design and implement end-to-end testing strategies for data pipelines and data products Define testing approaches for ingestion, transformation, enrichment, aggregation, and reporting layers Perform source-to-target validation, data reconciliation, and data integrity testing Establish automated data quality checks and validation frameworks Develop testing solutions for large-scale data processing systems and high-volume datasets Drive monitoring, observability, and quality controls across data platforms Collaborate closely with Data Engineering teams to identify quality gaps and improve platform reliability Build SQL and Python-based automation frameworks for data validation Define quality metrics, test coverage, and release readiness criteria Mentor QA engineers and promote quality engineering best practices Required Skills Data Engineering QA, Data Pipeline Testing, ETL Testing, ELT Testing, Data Integrity Testing, Data Validation, Data Reconciliation Testing, Source-to-Target Validation, Advanced SQL, Data Warehouse Testing, Snowflake, Databricks, Apache Spark, Apache Airflow, Python, Data Quality Frameworks, Test Strategy, Quality Engineering, Pipeline Monitoring, Data Observability, Automation Testing, CI/CD, Root Cause Analysis Good to Have Experience in Trade Performance Analytics or Financial Data Platforms Experience with Capital Markets or Investment Banking domain Experience with modern Data Observability tools Experience working in SaaS or Product-based organizations What We Look For Strong ownership mindset with a hands-on approach Ability to design quality strategies rather than only execute test cases Experience working directly with Data Engineering teams Strong analytical and problem-solving skills Ability to validate complex data transformations and business rules Excellent stakeholder management and communication skills Why Join Us Work on large-scale AI and data-driven healthcare products Own quality for mission-critical data platforms High-impact role with strong visibility across engineering and product teams Collaborative, fast-paced, and innovation-driven culture Opportunity to solve complex data quality and reliability challenges at scale Apply Now!!! If you are passionate about Data Quality, Data Engineering, and building reliable data products that create real-world impact, we would love to connect with you.