Lead BI quality efforts by designing testing strategies, validating data from sources through visualization, overseeing BI metric validation, building automated test frameworks, mentoring QA engineers, and managing defect resolution to ensure production readiness.
Key Responsibilities
- Strategy & Planning: Design comprehensive testing strategies, test plans, and test cases tailored for complex data ecosystems and ETL architectures.
- Data Validation: Validate end-to-end data accuracy from source systems to target databases, data warehouses, and visualization layers.
- BI Testing: Oversee the validation of analytics, metrics, and KPI visualizations in BI tools like Power BI, Tableau, or Looker.
- Automation: Lead the development and maintenance of automated testing frameworks for API validations, data consistency checks, and ETL workflows (e.g., using Python, Playwright, or Selenium).
- Team Leadership: Mentor, manage, and coach junior QA engineers and SDETs, defining performance metrics and evaluating release readiness.
- Defect Management: Act as the primary escalation point for data anomalies, logic defects, and system integration issues, ensuring resolution prior to production.
- Collaboration: Partner with Data Engineers, Product Managers, and Business Analysts to clarify acceptance criteria and ensure "shift-left" data quality.
Essential Qualifications
- Experience: 8+ years in Quality Engineering or BI/Data testing, with at least 1-2 years in a leadership or senior capacity.
- SQL & Data Skills: Advanced proficiency in writing and executing SQL queries to profile, compare, and validate large volumes of data across various sources.
- BI Tooling: Hands-on experience testing BI tools (e.g., Tableau, Power BI) and understanding of data modeling (star schemas, dimensional models).
- ETL Testing: Familiarity with ETL processes, data pipelines, and workflow orchestration tools (e.g., Apache Airflow, dbt).
- Automation & Scripting: Strong understanding of automation frameworks and scripting languages like Python is desirable.
- Methodologies: Solid grasp of agile/Scrum methodologies and continuous integration/continuous deployment (CI/CD) pipelines.
Common Educational Background
- Bachelor's degree in Computer Science, Information Technology, Data Analytics, or a closely related technical field
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