Quality Engineer in Bangalore, भारत

Key Responsibilities:

Quality Engineering & Test Strategy:

• Define and implement end-to-end quality engineering strategies for data platforms, pipelines, and analytics solutions.
• Establish testing standards for data ingestion, transformation, MDM, and consumption layers.
• Drive a shift-left quality mindset , embedding testing early in the development lifecycle.
• Define test coverage, quality gates, and acceptance criteria aligned with business and regulatory requirements.

Data Quality & Validation:

• Design and execute data validation frameworks to ensure accuracy, completeness, consistency, and timeliness.
• Validate master data domains (e.g., Client, Account, Advisor, Product) and downstream analytics datasets.
• Partner with Data Governance teams to align quality rules with business definitions and stewardship processes.
• Support reconciliation, controls, and audit requirements for regulated datasets.

Test Automation & Tooling:

• Build and maintain automated test frameworks for data pipelines, APIs, and analytics outputs.
• Automate regression, smoke, and data quality tests integrated with CI/CD pipelines.
• Leverage SQL, Python, and data testing tools to validate complex data transformations.
• Enable automated testing for Databricks, Azure data services, and MDM platforms.

Platform & Integration Testing:

• Validate end-to-end data flows across source systems, MDM, Databricks Lakehouse, and BI tools.
• Perform performance, scalability, and reliability testing for large-scale data pipelines.
• Support UAT by partnering with business users and Product Owners to ensure requirements are met.
• Assist with production readiness, release validation, and post-deployment verification.

Collaboration & Continuous Improvement:

• Work closely with Data Engineers, MDM Engineers, Product Owners, and Business Analysts to resolve quality issues.
• Provide guidance and mentorship to engineers and analysts on quality best practices.
• Analyze defects and incidents to identify root causes and drive preventive improvements.
• Continuously improve QE frameworks, tools, and processes.

Technical Responsibilities (Hands-On):

• Develop data quality and validation scripts using SQL, Python, and Spark-based frameworks .
• Validate Databricks Lakehouse solutions built on Delta Lake.
• Test MDM configurations, matching rules, survivorship logic, and publishing processes.
• Integrate quality checks into CI/CD pipelines and automated deployment workflows.
• Monitor and report on quality metrics, trends, and risks.

Qualifications:

• 7+ years of experience in Quality Engineering, QA, or Test Automation roles, with a strong focus on data platforms.
• 3+ years of hands-on experience testing data pipelines, data warehouses, or lakehouse architectures .
• Strong proficiency in SQL and experience using Python for test automation and validation.
• Experience with cloud-based data platforms, preferably Azure and Databricks .
• Solid understanding of data engineering concepts, data modeling, and ETL/ELT processes.
• Experience working in Agile delivery environments.

Preferred Experience:

• Experience testing MDM platforms (e.g., Profisee) and master data domains.
• Experience in financial services or regulated industries .
• Familiarity with data governance, data catalogs, and metadata management tools.
• Experience with API testing and validation of downstream data consumers (BI, reporting, analytics).