Job Title: Senior Quality Assurance Engineer
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).
