TE-1521- Data Engineer

Location: Hyderabad, India

Department: Software Development

Experience: 8-12

• Warehouse architecture and modelling — BigQuery (or equivalent) as the single source of truth, designed to serve both analytics and activation use cases.
• dbt project ownership — model layering (staging → intermediate → marts), naming conventions, tests, documentation, exposures, and the promotion workflow from dev to production.
• Ingestion pipelines — event streams from the CDP (Segment or equivalent) landing cleanly in the warehouse, plus structured loads from the product database, billing / payments, ad platforms and third-party SaaS systems.
• Identity resolution in the warehouse — stitching anonymous, edge-level and identified user records into one canonical key, and publishing that key to downstream consumers.
• Core marts — revenue, orders, subscriptions, cohorts, LTV, funnel conversion, channel performance, clinical or operational metrics as the business requires.
• Reverse-ETL / activation models — warehouse tables purpose-built to feed ad-platform conversions APIs, lifecycle marketing, audiences and experimentation tools.
• Data quality and observability — dbt tests, freshness monitors, row-count alerts, reconciliation checks between systems (for example: warehouse orders vs payment processor, warehouse conversions vs ad-platform reports).
• Data governance — PII / PHI handling, column-level masking, row-level security where required, access policies, and the documentation that makes those policies auditable.
• Orchestration and CI/CD — scheduled runs, retries, incremental strategies, pull-request-based dbt reviews, automated tests in CI.
• Partnership with external data partners and the sister MarTech Engineering function — clear ownership lines, shared conventions, joint on-call for pipeline incidents.

• 8–12 years in data engineering or analytics engineering at consumer-facing digital businesses — ideally including at least one direct-to-consumer, e-commerce, subscription or marketplace product.
• Deep, hands-on experience designing and maintaining a warehouse on BigQuery, Snowflake or Redshift. You can explain partitioning, clustering, materialised views, and cost / performance trade-offs from memory.
• Production experience with dbt — Core or Cloud — including model layering, incremental strategies, snapshots, tests, documentation, exposures, and managing a multi-contributor repo through pull requests.
• Fluent SQL — advanced window functions, CTEs, pivoting, session stitching, slowly-changing-dimension patterns. You can read a 200-line query and tell us what is wrong with it.
• Experience designing identity resolution in the warehouse — merging anonymous, cookie-level, logged-in and server-generated identifiers into one canonical customer key, and publishing that key for downstream systems to join on.
• Hands-on experience building marts for revenue, orders, subscriptions, cohorts, LTV and funnel analysis — and reconciling those marts against authoritative systems (payment processors, billing, ad platforms).
• Python for data work — transformations, orchestration DAGs, small internal tools, and scripting around the warehouse. You do not need to be a backend engineer.
• Working knowledge of at least one workflow orchestrator — Airflow, Prefect, Dagster, or equivalent — and the operational discipline that goes with it (idempotency, backfills, retries, alerting).
• Comfort integrating warehouse output into reverse-ETL tooling (Polytomic, Hightouch or Census) so that audiences, conversion events and customer traits can be activated in ad platforms, lifecycle tools and experimentation systems.
• A bias for data contracts and tests over heroics — you treat a broken dashboard as a platform failure to be prevented, not a one-off to be patched.
• Experience operating under ambiguity — a platform migration, re-platforming, or a major tracking overhaul where the "right" schema had to be invented mid-flight.

• Healthcare, fintech, or other privacy-sensitive domain experience — designing a warehouse that respects PII / PHI boundaries and survives audit.
• Experience operating a dbt repo shared between an internal team and an external data partner (or vendor) — conventions, code review, ownership boundaries.
• Experience with Segment Unify, identity-graph services, or custom identity-resolution frameworks.
• Hands-on with experimentation data — building the exposures, assignment and outcome tables that power A/B test readouts and feeding them to an experimentation platform.
• Hands-on with LTV or propensity modelling (regression, survival or tree-based), and with getting those predictions into the warehouse as first-class tables.
• Familiarity with streaming patterns — Pub/Sub, Kafka, Kinesis — and when to prefer them over batch.
• Prior early-hire data platform experience at a growth-stage product company, where you set the conventions rather than inherited them.

• You treat the warehouse as a product — versioned, documented, tested, with clear ownership of every model and every downstream exposure.
• You are as comfortable debugging a BigQuery cost regression as you are explaining an attribution or LTV model to senior leadership.
• You write dbt tests before you write dashboards. You would rather catch the problem in CI than in a Monday standup.
• You draw scope lines honestly — you know where your layer ends and the MarTech, product or ML layer begins, and you document the hand-off.
• You reconcile aggressively — warehouse vs payment processor, warehouse vs ad platforms, warehouse vs the product UI. Anything that does not tie out is a ticket, not a footnote.
• You write things down. The team should not need to re-learn a modelling decision you have already made.

• Location: On-site at our Hyderabad (Softobiz) office. This is not a remote role.
• Reporting: Reports to the Engineering Owner, with regular working interactions across product, analytics, growth and external data partners.
• Compensation: Competitive base salary plus performance bonus and standard benefits.
• Schedule: Full-time, with some overlap expected with partner teams in other time zones.