Software Engineer - Data

Software Engineer - Data

Wire up Faym's data layer end-to-end. Managed tools where they save weeks, custom code where they don't.

About Faym & the role

Faym is a creator-commerce platform connecting India's next generation of creators with leading e-commerce brands, built on MongoDB, Node.js, and GCP. We're hiring our first dedicated data person to build Faym's analytical data layer end-to-end — wiring operational data into a warehouse on BigQuery, modeling it with dbt, and unblocking the analytics team from building dashboards directly on MongoDB. This is not a pure data engineering role: it's a software engineer who has done data work — comfortable in Python, but pragmatic enough to use managed ELT tools when they save weeks of custom infrastructure.

What you'll do

Set up ingestion via managed ELT. Deploy a managed ELT platform (Hevo, Airbyte, Fivetran — you pick) for MongoDB and our mobile attribution platform into BigQuery; write Python connectors for partner feeds where managed tools don't fit.

Model with dbt. Bronze/silver/ gold layers, star schema, tests, and docs. Build the reconciliation marts that close the 8–10% delta between our internal numbers and top Indian e-commerce partners.

Stand up analyst tooling. Deploy a BI tool (Metabase to start), set up RBAC, and migrate existing direct-MongoDB dashboards onto BigQuery.

Own orchestration & quality. Schedule pipelines, add dbt tests, freshness monitors, and alerting. Git, code review, CI — treat this like software, not scripts.

What we need-

Must have

• 2–4 yrs software engineering with ≥1 yr data work

• Strong Python and advanced SQL (window functions, CTEs)

• Hands-on dbt — sources, models, tests, incremental patterns

• Production use of one managed ELT (Hevo, Airbyte, Fivetran, Stitch)

• At least one cloud warehouse (BigQuery, Snowflake, Redshift, Databricks)

• Software engineering hygiene — Git, code review, CI

Nice to have

• BigQuery — partitioning, clustering, slot economics

• MongoDB experience, especially querying

• GCP familiarity (Cloud Functions, GCS, Cloud Run, IAM) • Orchestration tools (Airflow, Dagster, Prefect)

• E-commerce, affiliate, or creator-economy background

• BI tools (Metabase, Looker) or reverse-ETL exposure

What success looks like

30 days - First ELT pipeline live (MongoDB → BigQuery). dbt scaffolded. KPIs catalogued.

60 days - Mobile attribution events and at least one partner feed flowing. Bronze + early silver complete. First reconciliation query published.

120 days - Gold layer live with star schema. BI tool deployed. The analytics team serves for the majority of queries.

Tech stack

Warehouse & modeling - BigQuery · dbt · star-schema

Ingestion - Managed ELT (Hevo / Airbyte / Fivetran) · Python connectors

Operational systems - MongoDB · mobile attribution platform · partner feeds

Analyst-facing - Metabase · service-account RBAC

Workflow - Python · SQL · Git · GitHub Actions

Email ID - [email protected]