Applied Retention is building an AI decisioning layer for retention marketing. You are the founding engineer for its data substrate and nightly runtime, working directly with the CTO. You design the schema, build the ingestion, write the nightly computations the product decides on, and make it correct enough that brands bet real marketing spend on it.

Roughly 70% data engineering, 30% backend.

What you'll build
Ingestion pipelines, multi tenant from multiple async data sources
Nightly batch computation over millions of events as set-based SQL and dbt models
A multi-tenant data model where one brand's data never affects another's run
Backfill, testing infrastructure and replays as designed capabilities
Vendor integrations built for failure: retries, idempotency, reconciliation

Must have
46 years building and operating production systems




Deep PostgreSQL: schema design, query plans, batch performance on tables in the tens of millions of rows
Owned a data pipeline in production end to end: ingestion, transformation, reconciliation, serving
Set-based thinking: multi-step SQL, window functions, percentiles, incremental computation; you've turned raw event or order data into cohort, retention, or funnel metrics
Production Python, any framework
You test batch jobs and reconciliations
Transparent written communication
Able to start within 30 days

Nice to have dbt • Django in production • AWSStack: Python, Django, PostgreSQL, dbt, React with TypeScript, AWS.

📌 Senior Data Engineer Immediate Joiners Only Navi Mumbai (India)
🏢 Applied Retention
📍 India