Summary

We are hiring a Senior Data Analyst to own the correctness of our internal analytics platform — the data models, metric definitions, and AI answer layer that the whole company runs on. You will define metrics, chase down why numbers move, automate the repetitive analysis, and work directly with Revenue, CS, Finance, and Exec teams. Strong SQL and data modelling are required.

Real, hands-on use of AI tools — as a working tool, not as something you have read about — is equally required. Dashboard tooling is learnable and is not what we are screening for.

What You'll Do

- Define and document the company's core metrics so every team measures the same way.
- Own and tune the semantic model behind the platform's AI answers.
- Investigate metric changes and find the cause.
- Write and tune SQL against Snowflake, including for cost and performance.
- Build AI agents and workflows that take over repetitive analysis.
- Put data quality checks in place so wrong numbers get caught early.
- Build and migrate dashboards, validating against source data.
- Implement access correctly and handle sensitive employee data carefully.
- Explain findings to non-technical people in plain language

What We Need

Must haves

- 3–5 years in data analytics, analytics engineering, or applied data science at a product or SaaS company.
- Strong SQL — CTEs, window functions, complex joins,



and performance work on large tables.
- Data modelling depth — grain, star schemas, slowly changing dimensions, and why a model breaks. Not just querying.
- Cloud warehouse experience. Snowflake preferred; another warehouse is fine if the depth is there.
- Daily LLM use in your actual work, with at least one workflow you automated and can walk through in detail.
- Statistical literacy — sampling, significance, seasonality, and the difference between correlation and cause.
- Careful with detail. You notice when a number is off by a little and chase it.

- Clear, plain communication, written and spoken.

Good to have

- Python for data work, analysis, or automation.
- Analytics engineering tooling (dbt or similar) and pipeline exposure.
- Agent frameworks, RAG, or evaluation loops.
- BI tooling — Power BI, Streamlit, or similar.

Helpful, not required.

- SaaS metric fluency — MRR, ARR, NRR, churn, cohorts, retention.
- Product analytics tools.
- Exposure to a product-management org, or interest in the PM track this role leads to.

The role is not for you if

- You want a narrow, well-defined scope with one team and one workstream.
- Your AI experience is occasional chat use.
- You want pure modelling or ML research work.

This is not that role.

- You are looking for a queue of tickets rather than a set of outcomes

📌 Senior Data Analyst (Hyderabad)
🏢 Keka HR
📍 Hyderabad