Senior Data Engineer - Data Platform
- Compensation
- Not disclosed
- Location
- India, IN
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5+ years building production data platforms in business-critical environments, with architecture you've owned end to end and 2+ years experience with Snowflake
- Worked with billions of rows — you know what breaks at that scale and how to design pipelines and checks that don't
- Shipped LLMs inside data pipelines as production systems — extraction, enrichment, or validation with structured outputs, versioned prompts, and labelled eval sets. You can show the repo
- Designed eval harnesses others depend on — scorers, labelled sets, judge calibration, regression suites; you can talk through what they caught and what they missed
- Sharp judgement on rules vs. LLMs — you reach for a dbt test or SQL first, defend the call either way, and have codified the standard for a team
- Expert Python and SQL — production-grade, performance-aware, comfortable at very large scale
- Expert dbt and Snowflake — modular transformation layers with real business logic; warehouse design, query optimisation, clustering, RBAC, cost at scale
- Extensive Airflow — orchestration, dependency management, recovery patterns, cost optimisation in production
- Solid AWS — S3, Lambda, Glue, ECS, RDS
- AI coding tools are how you work — Claude Code, Cursor, or equivalent, daily, with real shipped work to show for it
- Architecture judgement and a product mindset — when to refactor, optimise, ship, or redesign; you care how data quality lands with customers, not just uptime
Full description
Firmable is the market-leading B2B sales intelligence platform in Asia Pacific — and we're scaling that success globally at pace. Backed by leading investors and 2,000+ customers strong, we exist to give sales teams an unfair advantage: the deepest company and people data of any platform, enriched with real-time signals, served at the right moment by intelligent agents.
Our data is the product. Building it now means building with LLMs in the pipeline, and setting the standard for when to trust them.
The Role
As Senior Data Engineer — Data Platform, you'll architect the transformation and warehouse layer that turns billions of raw records into the world's most accurate B2B dataset, and design how LLM-based extraction, enrichment, and validation run inside it as production infrastructure.
You make the architecture calls: model design and materialisation, warehouse cost and performance, orchestration patterns the team builds on, and the rule-vs-LLM standard for every quality and enrichment step. You know when a dbt test does the job, when an LLM check is needed, and when both belong together.
This is hands-on senior engineering with sharp architecture judgement. You write the reference implementation, then the team builds on it.
What You'll Own
- LLM-in-the-pipeline architecture — the patterns for extraction, enrichment, entity resolution, and semantic validation over millions of records a day: structured outputs, retries, human-review fallbacks, and the abstractions others reuse
- Eval harnesses — design the labelled sets, scorers, judge calibration, and regression suites that gate every prompt or model change; own precision/recall targets per check
- The rule-vs-LLM standard — deterministic checks (dbt tests, data contracts, SQL) wherever structure allows; LLMs only where semantic judgement is needed; codified so the team can apply it without you
- Cost and drift — token budgets per pipeline, model routing (cheap models for classification, stronger ones for hard cases), drift detection on vendor updates, and the token math behind the ceilings
- Observability — every LLM call logged with prompt version, model, cost, latency, and decision, alongside pipeline alerting that surfaces problems before they cascade
- Warehouse and transformation architecture — dbt layers that encode real business logic; Snowflake performance, clustering, materialisation, and cost over billions of rows
- Orchestration and infrastructure — Airflow patterns for recovery and cost-aware scheduling; AWS infrastructure as code