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Tier-1 brand and a mid-level, popular analytics-engineer role with general SQL/BI skills drives high competition.
Strong SaaS and Customer Success domain knowledge preference reduces transferability moderately.
Requires SaaS experience, production-grade Snowflake/SQL and BI expertise, making shortlisting fairly strict.
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Develop and maintain production-grade SQL models, data pipelines, and interactive dashboards in Snowflake, Power BI, and Tableau to support retention, expansion, and growth business decisions.
Collaborate with Data Engineering, Data Science, and business teams to define reliable metrics, ensure data quality, and translate complex data into actionable insights across Customer Success, Support, Finance, and executive audiences.
Leverage AI-assisted analytics tools and Python for data exploration, validation, automation, and quality control within a Git-based version-controlled environment following engineering best practices.
Minimum 3 years of relevant experience in a SaaS environment.
Strong SQL skills with hands-on experience using Snowflake or comparable cloud data warehouses.
Proven experience developing and maintaining production-grade analytics assets, not just ad hoc reporting.
Proficiency in Microsoft Power BI and/or Tableau for designing interactive dashboards and developing reusable business metrics.
Analytical engineer with strong technical expertise in scalable analytics engineering combined with deep understanding of SaaS business metrics like retention, expansion, ARR, and customer engagement.
Experience working cross-functionally with Customer Success, Support, Finance, and Product teams delivering validated, governed analytics solutions with clear executive communication.
Comfortable integrating AI-assisted analytics tools responsibly and automating workflows, while adhering to collaborative software development practices and continuous improvement.