Read this first. This role is not for you if:
• Your LLM work is limited to ChatGPT, Copilot autocomplete, or a notebook demo that never ran in production
• You've never measured how often an LLM step in your pipeline gets it wrong
• You've only worked with tables of thousands of rows, not millions or billions
• You've only built pipelines in drag-and-drop ETL tools (Informatica, SSIS, ADF), not in code
• You've never owned a production pipeline, including fixing it when it breaks
• Your experience is mostly services or consulting delivery, moving between client projects without long-term ownership
• You need detailed specs, fixed hours, or close supervision
• You don't use AI coding tools daily, or you can't show work you shipped with them Firmable sells company and people data to sales teams across Asia Pacific and the US, and we're taking it worldwide. The hard problem is keeping billions of records right while companies hire, restructure, move and rebrand every day. More and more of that work runs through models and agents we build into the pipeline. What you'll work on You'll build and run the pipelines that turn raw records into the datasets our product and agents use. Some of it is SQL, dbt, Airflow and Snowflake done properly. The rest is getting model steps inside those pipelines to behave:
• LLM extraction, enrichment and matching that run over millions of records a day, with structured outputs, retries and a human-review path
• Eval sets that tell you how often each step is wrong,



and regression tests that run on every prompt or model change
• Sending simple calls to cheap decision models and hard ones to stronger LLMs, with token cost tracked per pipeline
• Company and people deduplication across every market we cover
• Airflow DAGs that recover on their own, and Snowflake models that stay inside budget Must haves
• 3–5 years in data engineering
• Robust Python and SQL
• Production dbt and Airflow, and a cloud warehouse (Snowflake preferred) Nice to have: Logfire or OpenTelemetry
· embeddings or fuzzy matching for entity resolution
· fine-tuning small models
· AWS and PostgreSQL
· Spark or streaming
· GDPR/SOC2 How we build We write code with coding agents like Claude Code by default, and engineers spend their time on design, review and evals. In production we pick the model for the job: frontier LLMs for messy extraction and reasoning, small typed decision models when the answer is a yes/no, a choice or a score, and MCP servers and agent SDKs when a model needs tools. When something new ships, we run it against our evals that week. Every LLM call is logged with its prompt version, model and cost. Small teams, no layers, weekly releases moving to daily. The deal Competitive base and meaningful equity. Eval coverage and cost controls for our in-pipeline models are mostly unbuilt, and you'll ship a lot of it. Firmable is an equal opportunity employer. We believe diverse teams build better products.

📌 Data Engineer (India)
🏢 Firmable
📍 India