Location: Indiranagar, Bangalore
Work Arrangement: Full-time, Work From Office
Experience: 4–6 years
About M
At M - we’re early, ambitious, and moving fast. We’re building a new category in how urban households function. We help households run smoothly through trained professionals, operational excellence, and technology-driven support systems. Backed by Peak XV Partners, Blume Ventures and CRED we’re reimagining how homes run, so people never have to think about chores again.
The Role
Numbers only help if people trust them : own that.
M runs on a signal that doesn't arrive in neat rows. Orders, behaviour, and above all conversations - most of what we know about a household came from someone talking. Turning that into metrics the company can actually act on means modelling ambiguity honestly, not flattening it.
You're the first dedicated analytics engineering hire. There's no legacy mess to untangle here, which is the point: you decide what the model layer, the metric definitions and the quality bar look like, before the volume arrives. You'll work directly with the analytics lead, and closely with product, ops and engineering.
What you'll do
• Own the model layer end to end ; staging through marts, dimensional modelling, the single source of truth every dashboard and analysis points at.
• Model conversational data: LLM-assisted extraction, classification and enrichment that turns chat into something queryable, with confidence and uncertainty kept visible rather than quietly rounded away.
• Define metrics where ground truth is fuzzy ; decide what "correct" means for probabilistic outputs, and build the sampling and eval checks that keep those definitions honest as the product changes.
• Make data trustworthy: tests, freshness SLAs, source contracts, alerting. Broken data gets caught before it reaches a dashboard, not after someone quotes it in a review.
• Build the semantic layer that agentic analytics sits on. Weak definitions don't produce a wrong chart ; they produce a confident wrong answer.
• Partner with engineering on event design and instrumentation so new features ship analytics-ready.
• Automate the repeatable - reporting, monitoring, documentation, and agentic workflows that cut manual effort and discovery friction.
• Set the standards and write the docs the next three hires build on.
What We're Looking For
• 4–6 years in analytics engineering, data engineering, or a data-heavy analyst role.
• Strong hands-on dbt ; models, tests, macros, sources, CI/CD. You've owned a real project, not just run commands in one.
• Advanced SQL: window functions, CTEs, incremental logic, performance tuning.
• Solid dimensional modelling instincts -grain, star schemas, SCDs and opinions you can defend.
• Working Python for validation, transformation and pipeline support.
• Comfort with messy, semi-structured data, and the judgement to know when a probabilistic output is solid enough to build a metric on.
• You treat data quality as a product concern, not a cleanup task.
• Hands-on LLM use for extraction, classification or enrichment at scale.
Bonus Qualifications
• Semantic / metric layers, or agent-based analytics tooling.
• Text or conversational data at volume; event-first modelling patterns.
• Catalog and lineage tools (OpenMetadata, DataHub, Amundsen).
