QA Lead - Data

Hilabs; Karnataka; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

HiLabs is seeking a hands-on QA Lead / Manager, Data Engineering to define and drive the quality strategy for our data platforms and productization initiatives. This role requires deep expertise in data testing, data quality, and observability.

You will partner with Data Engineering, Product, and Business teams to ensure accuracy, reliability, and release readiness of data products, acting as a quality owner with a strong second-eye approach.

Qualifications

• Experience in designing data quality strategies for large-scale data platforms.
• Ability to validate complex data transformations end-to-end across pipelines.
• Strong knowledge of data governance, lineage, and reconciliation techniques.

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 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.

Skills

Data Engineering QA

Data Pipeline Testing

ETL Testing

Data Integrity Testing

Advanced SQL

Python

Data Quality Frameworks

CI/CD

Observability

Root Cause Analysis

Tools

Snowflake

Databricks

Apache Spark

Apache Airflow

Job description

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

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.