Data Analyst / Analytics Engineer (Full-Stack Data)

MillD · Full-time · 4–7 years' experience

About the role

MillD runs on data across every function — marketing, supply chain, customer care, and leadership — but that data lives in a dozen disconnected systems. We're looking for someone to build our data foundation from the ground up: the pipeline that pulls it all together, the single source of truth on top of it, and the dashboards that let every team see what's happening without asking.

This is a build-it-yourself role. You'll own the full stack — ingestion, warehouse, modelling, and BI — and you'll also be the person who answers the hard questions across the business. Equal parts builder and analyst.

What you'll own

- The data pipeline — build and own ingestion from every source system (Shopify, GoKwik, Meta and Google Ads, GA4, quick-commerce and marketplace platforms, WMS/ERP for supply chain, and customer-care tools) into a central warehouse, with a clean transformation layer and reliable scheduling.
- A single source of truth — define canonical metrics, reconcile numbers across systems that disagree, and document the caveats so the whole company trusts one set of numbers. This is the first job, before any new dashboards.

- Dashboards & self-serve — build and maintain dashboards for each function so teams get answers on their own: marketing (CAC, MER, cohorts, channel and geo, creative), supply chain (inventory, fill rate, out-of-stock, demand-planning inputs, PO tracking), and customer care (contact volume, resolution, abandoned-checkout recovery, CSAT).
- Cross-functional analysis — the deep-dives that don't fit a dashboard: retention curves, unit economics, attribution reconciliation, geo experiments, forecasting inputs.




- Data quality & governance — monitoring and alerting so breakages are caught before they mislead a decision; ownership of definitions and documentation.
- Enablement — make the organisation self-serve; train stakeholders to read and use the data correctly.

What we're looking for

- 4–7 years in analytics, analytics engineering, or data — with a track record of building a pipeline and BI layer from an early stage, not just working inside a mature one.
- Solid SQL; hands-on with a cloud warehouse (BigQuery, Snowflake, or Redshift), a transformation workflow (dbt or equivalent), data connectors / ETL (managed tools like Fivetran/Airbyte, or Python), and a BI tool (Looker Studio, Metabase, Power BI, or Tableau).
- A source-of-truth mindset — you reconcile numbers, question platform-reported figures and over-attribution, and document what a metric does and doesn't mean, rather than plotting whatever a tool hands you.
- Comfort turning messy, multi-source data into something trusted, and working across very different business functions.
- D2C / e-commerce data experience (Shopify, ad platforms, GA4) strongly preferred.

This is not the right role if you're a dashboard-only analyst who needs clean data handed to you, or a pure data engineer with no business-analysis instinct — this role builds the pipes and answers the questions. Nice to have

- dbt and Python for data work; GA4 → BigQuery specifically. Power BI and Looker familiarity.
- Supply-chain or demand-forecasting analytics exposure.
- Experience standing up a company's first data stack.
- Familiarity with quick-commerce / marketplace data.

How success is measured A trusted single source of truth, live and adopted; each function on self-serve dashboards; time-to-answer on business questions down sharply; known reconciliation gaps closed. Specific targets are set in a 30/60/90 plan on joining.

Pay: ₹800,000.00 - ₹1,500,000.00 per year

Work Location: In person

📌 Data Analyst (Full Stack) (Bengaluru)
🏢 MILLD
📍 Bengaluru