Description : Quality Engineer (Data & BI)

Location : Pune (Offshore Onsite)

Duration : 6 Months (Contract)

Role Summary :

We are seeking a highly technical Quality Engineer specializing in BI, Data Analytics, and Data Warehouse environments to ensure the integrity of our enterprise data platforms. In this role, you will be responsible for the end-to-end validation of complex data pipelines, from source system analysis in Oracle and Snowflake to front-end dashboard validation in MicroStrategy (MSTR) and Power BI. You will leverage advanced SQL and Python scripting to build robust automation frameworks that validate business rules and dimensional models. A deep understanding of Azure data pipelines and ETL workflows is essential to ensure data accuracy as it flows through the ecosystem. The ideal candidate will combine rigorous technical testingincluding performance and regression suiteswith the ability to document comprehensive test strategies for high-stakes retail customer data.

Responsibilities :

- BI Dashboard Validation : Conduct meticulous testing of MicroStrategy (MSTR) and Power BI reports, ensuring the accuracy of the semantic layer, metrics, and visualization logic.

- Data Warehouse Testing : Validate relational structures and dimensional models (Star/Snowflake schemas) within Snowflake and on-prem Oracle environments.

- End-to-End Pipeline Validation : Trace and validate data flow through Azure data pipelines, ensuring data integrity from ingestion to the final reporting layer.

- Automation Framework Development : Design and implement test automation frameworks using Python, Unix scripting, and SQL to streamline repetitive data quality checks.

- Source-to-Target Mapping : Perform deep source system analysis and validate complex business rules to ensure data transformations align with functional requirements.

- Performance & Scalability Testing : Execute performance testing on BI solutions to identify bottlenecks in query execution and dashboard rendering times.

- Regression Testing : Maintain and execute comprehensive regression suites to ensure new data loads or schema changes do not impact existing reporting assets.

- Data Quality Governance : Implement automated data quality checks to monitor for null values, duplicates, and referential integrity breaches across environments.

- Defect Life Cycle Management : Document detailed test strategies, report defects with technical logs, and collaborate with cross-functional teams to drive resolutions in an Agile setting.

- Retail Domain Analysis : Apply domain-specific knowledge to validate customer data, loyalty metrics, and retail-specific KPIs (Preferred).

Technical Requirements :

- BI Tool Proficiency : Extensive hands-on experience in testing MicroStrategy (MSTR) and Power BI dashboards and semantic layers.

- Advanced SQL Mastery : Expert-level SQL skills for complex data profiling, validation, and backend testing.

- Data Architecture Knowledge : Strong understanding of Data Warehousing concepts, including ETL processes and dimensional modeling.

- Cloud & Database Expertise : Technical familiarity with Snowflake, Oracle, and Azure Data Factory/Pipelines.

- Automation & Scripting : Proven experience using Python or Unix shell scripting for building data automation tools.

- QA Methodology : 3 to 5+ years of specialized experience in Quality Engineering within data-centric environments.

Preferred Skills :

- Retail Analytics : Experience handling large-scale retail customer datasets and understanding retail business logic.

- Cloud Testing Tools : Familiarity with automated data testing tools compatible with the Azure ecosystem (e.g., Great Expectations, dbt tests).

- Agile Leadership : Ability to work independently, manage multiple priorities, and contribute to sprint planning in a fast-paced environment.

- Communication Excellence : Strong ability to translate complex data discrepancies into clear business-impact reports for stakeholders.