Hey, I'm Mohit.

I'm an analytics engineer and solutions architect. I design data platforms and turn product and usage data into metrics teams trust: app data modelled in dbt, clear metric definitions, and dashboards people can use on their own.

I've spent 13 years in software and data, the last 7 building analytics platforms end to end, at an early-stage AI startup, an industrial energy company and for a private equity firm's portfolio companies. The part I enjoy most is the moment a team agrees on what a metric means and starts using it to decide.

I'm open to analytics engineering, data architecture and product analytics roles, in India or remote. Reach me by email, on LinkedIn, or read my CV.

Experience

• Product analytics for an AI SaaS product
  Set up the data stack from scratch and gave the product team self-serve reporting on signups and usage of every AI feature.
  Early-stage AI startup, built solo
• Trusted metrics in a lakehouse
  Owned lakehouse models with medallion layers, SCD Type 2 history and data quality rules, so business metrics stayed consistent.
  Industrial energy company
• One set of metrics across several businesses
  Consolidated ERP data from several companies into one warehouse, with shared metric definitions and four-tier row-level security.
  A private equity firm's portfolio companies

Earlier:

• Automated a data catalog with PII tags, quality metadata and lineage across BigQuery and Snowflake.
• Built a Python framework to match and clean customer master data across systems.

Full experience on the CV →

How I work

• Measure every feature, not just signups
  Tracked signups and usage of every AI feature at an early-stage AI startup.
• Self-serve beats a ticket queue
  Set up Metabase self-serve reporting for the product team on top of dbt models.
• Keep history, so last quarter's number never changes
  SCD Type 2 history in the lakehouse models at an industrial energy company.
• Check the data before it reaches a dashboard
  Data quality rules in Lightup on the same lakehouse.
• Decide who sees what once, in the model
  A four-tier row-level security model for Power BI across several businesses.

Now

• Focusing on product analytics for SaaS companies, and how AI can make product data easier to use
• Learning PostHog event tracking, funnel and retention metrics, and A/B test analysis
• Exploring text-to-SQL over a metrics layer, and how to evaluate it

Experiments

I use AI coding tools every day, and try ideas out as small experiments on GitHub, like an LLM research agent and a governed-data agent. They're learning projects, not production work. See them on GitHub →