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Hyderabad metro location increases competition but niche Snowflake/ThoughtSpot specialization reduces applicant density.
Core analytics engineering skills are transferable, though ThoughtSpot and asset-lifecycle domain knowledge increase specificity.
Multiple mandatory technologies (Snowflake, advanced SQL, dbt, Python) and BI tooling create moderately strict filtering.
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Own and optimize the transformation layer of the data stack using Snowflake, ensuring clean, performant data models powering ThoughtSpot and Power BI for hundreds of users.
Design and implement scalable Star and Snowflake schema data models and maintain modular, version-controlled transformation pipelines with Python and SQL.
Monitor and improve data quality, query performance, and enforce CI/CD practices in the analytics development lifecycle.
Advanced SQL skills including window functions, complex joins, and query plan analysis.
Proficient with Snowflake features (Time Travel, Zero-copy Cloning, Tasks, Streams) and warehouse cost management.
Strong Python skills for production-grade data manipulation and ETL automation scripts.
Experience building BI data models for ThoughtSpot and/or Power BI; hands-on with dbt or Dagster for managing data transformations.
Works effectively at the intersection of data engineering and BI, valuing model correctness, query efficiency, and BI user experience equally.
Experienced in cloud data warehouse performance tuning, data governance, and metadata documentation to ensure stakeholder trust.
Familiar with software engineering best practices applied to analytics (version control, code reviews, automated deployment).