Job Title: Founding Engineer - Data Products

Location: On-site, Bengaluru

Employment Type: Full-Time About the role

We're building the platform that turns raw enterprise data — Slack, Jira, GitHub, email, docs, tickets — into structured, machine-trainable products for AI labs. You'll own the data foundation end to end: how we ingest from many enterprise sources, normalize the mess into one common shape, resolve identities across systems, and assemble the structured "decision" units we sell.

This is a hands-on founding role on a small team. You'll make the core architectural calls, set the engineering patterns the rest of the team builds on, and get a real product shipping inside customer cloud environments fast. Responsibilities

Own the ingestion → normalization → entity-resolution → product-assembly pipeline architecture

Build and operate production connectors across multiple SaaS/API sources (starting Slack, Jira, GitHub; expanding to Google Workspace, Teams, Notion, Confluence, CRM)

Implement reliable incremental sync, pagination, retries, backfills, and checkpointing

Design the medallion data model (raw → normalized entities → sellable products) and the transformation layers between them

Build tiered entity resolution — authoritative and deterministic cross-system identity joins first, probabilistic matching later — with confidence and evidence retained

Assemble structured, citation-backed "task/decision" units from resolved data, seeded from concrete outcomes (merged PRs, closed tickets)

Handle schema evolution, deduplication, late/deleted data, and per-tenant workflow differences via config, not code forks

Build pipelines that are idempotent,



replayable, observable, and reproducible (versioned output manifests)

Establish data-engineering standards and patterns for the team

Work closely with the Privacy/PII founding engineer so the pipeline hands off cleanly at the trust boundary Must-have skills

6+ years of data/software engineering, including owning a production pipeline that ingested from multiple external APIs with incremental sync, retries, and schema drift (this is the non-negotiable – not "I've used a pipeline," but "I built and operated one")

Strong Python and SQL

Solid ETL/ELT experience across multiple SaaS platforms and APIs

Deep understanding of OAuth, pagination, rate limits, incremental/delta sync, backfills, idempotency, and failure recovery

Experience designing data models, normalization layers, and entity resolution across heterogeneous sources

Experience with a modern warehouse/lakehouse (Snowflake, BigQuery, Databricks, Redshift, or equivalent) and orchestration (Airflow, Dagster, Prefect, or equivalent)

Comfortable owning architectural decisions and setting patterns in a small founding team

Experience operating data workloads in cloud environments, ideally customer VPC / private-cloud deployments

Nice-to-have skills

Connector platforms (Airbyte, Meltano, Fivetran) and building custom API connectors

Entity-resolution tooling (Splink, Zingg) and record-linkage fundamentals

Identity/directory/HR data sources as an ER seed

Data lineage/catalog tooling (OpenLineage or similar)

Handling large volumes of semi-structured/unstructured data

Familiarity with AI training-data workflows (SFT, RLHF, evals) and what labs actually buy

📌 Founding Engineer - Data Products (Bengaluru)
🏢 LH2 AI Labs
📍 Bengaluru