Senior Product Analyst

Job location: Remote in India

About the role:
Sun King is looking for a Senior Product Analyst to be the analytical backbone of the CRM product team within Enterprise Products. In this role, you will sit close to the product managers, backend and data engineers, working at the intersection of product analytics and data reliability. You will help the team understand how the product is actually used and where it falls short, and you will be among the first to notice when the numbers behind those judgements cannot be trusted. The role will suit someone with technical depth who prefers to collaborate and work closely to a product and its users, is comfortable operating within an established data platform but can also do analysis from new data sources.

• Document the business rules our systems are supposed to obey, turn them into automated checks that catch contradictions across services, run the exception dashboard that keeps every open break visible and ageing, and extend the catalogue to each new system we integrate or new functionalities we build.
• Unstructured investigative querying across the warehouse, read replicas, Prometheus metrics and product events — spot-checking against source, sanity-checking new data, and chasing numbers that look wrong.
• Diagnose the reported data issues by starting from a symptom, checking in the CRM, querying read replicas, Grafana logs, CDC topics and work out whether the pipeline or the application caused it.
• Bring together data from new sources yourself and run one-time analysis in Python and Redshift, handing over persistent pipelines to data engineering.
• Build the CRM's business and product dashboards. Work from canonical warehouse tables and the company's standard metric and dimension definitions, helping define what each means for the CRM.
• Build the CRM's view of system reliability. Write PromQL against the metrics infrastructure already exposes and build product-facing reliability dashboards on top of them — you won't provision new instrumentation, but you will raise the gaps where a metric is missing.
• Set up alerts for business metrics crossing thresholds, perform checks on the metrics that matter after every release.
• Build a shared taxonomy for issues raised through various channels and perform analytics on them.
• Quantify the impact of issues, downtimes - to help the team understand the impact of issues and downtime, including customers affected and potential financial impact.
• Conduct usage, funnel, and cohort analysis; understand feature adoption through application data and Firebase; design and analyse A/B tests; whether a change actually worked, and making sure the app's events fire correctly and mean what we think they mean.

• A bachelor’s degree in engineering.
• 4+ years of relevant experience in product analytics, data quality, or business intelligence.
• Experience in a domain where wrong data costs money — lending, payments, fintech, insurance, logistics, SaaS or e-commerce operations.
• A track record of finding a problem nobody asked you to look for, and getting it fixed afterwards.
• Strong hands-on experience in SQL against messy production data, strong in Python.
• Experience with Redshift or another columnar warehouse, Postgres databases, NoSQL databases like MongoDB.
• Experience with BI or dashboarding tool, preferably Looker — enough to build and maintain a production dashboard set other people depend on.
• Experience managing A/B tests and done funnel and cohort analysis.
• Hands-on experience with Google Analytics and Firebase, not just aware of them.
• Understanding of how to pull data from a source that wasn't in the warehouse yet — an API, a vendor export, a production database — and loaded it yourself to answer a question. Doing this in AWS (RDS, S3) is a plus; querying ELB logs is a bonus.
• Knowledge of PromQL, or the equivalent in Datadog, Grafana or New Relic, to build system reliability dashboards.
• Understanding of how to use AI tools to build small automations and dashboards outside the BI tool.
• Clear written communication. Ability to write impact memos that engineering and finance team act on, and documentation a future teammate can use.
• Good stakeholder management skills, and comfortable to collaborate across teams you have no authority over, and the patience to keep making a case with evidence rather than volume.

Good to have:

• Understanding of change data capture and event streaming (Kafka) — enough to reason about replication lag, not to build the pipeline.
• Knowledge of a data quality framework — dbt tests, Great Expectations, Soda, Monte Carlo or similar.
• Pay-as-you-go, asset finance, microfinance or emerging-market lending.