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Metro location and known enterprise brand increase applicant density, but senior specialized data QA reduces broad competition.
Strong data warehouse, dbt, Snowflake, and analytics validation focus creates high domain-specific background sensitivity.
Specific 7+ years requirement, Snowflake/dbt/Python mandates and data QA ownership make filtering very strict.
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Own and define end-to-end QA and test strategy for enterprise data and analytics platforms with hands-on execution, including code contribution for data validation and pipeline monitoring.
Design, build, and maintain automated testing frameworks with SQL and Python for Snowflake data models, dbt transformations, and downstream analytics.
Lead application of AI-driven testing techniques and partner with Data Engineering to embed quality into ELT pipelines and workflows while communicating quality risks and metrics to leadership.
7+ years in Quality Engineering focused on data, analytics, or data warehouse testing.
Experience testing modern cloud data warehouses such as Snowflake and working with dbt transformations.
Proficiency in writing automation code using Python and SQL for testing and monitoring data platforms.
Bachelor’s degree in Computer Science, Engineering, or a related field; Office-based role in Pune (Hyderabad also mentioned).
Experienced in building and scaling data quality practices quickly within unfamiliar platforms, delivering measurable value in weeks.
Skilled in root cause analysis and proactive problem resolution with ability to identify coverage gaps and implement solutions independently.
Familiar with dimensional modeling, ELT architecture, data observability frameworks, AI-assisted testing tools, and validating analytics across business domains.