Senior Data Platform Engineer — LLM-Driven Pipelines

Carbon Data Solutions; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Firmable is an AI-native organisation building a market-leading data platform. We seek a Senior Data Engineer — Data Platform to 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 in production.

You’ll own LLM-in-the-pipeline architecture, eval harnesses, the rule-vs-LLM standard, cost and drift controls, and observability across the data stack.

Qualifications

• 5+ years building production data platforms with end-to-end ownership.
• Experience with billions of rows and scalable pipelines.
• Shipped LLMs inside data pipelines as production systems.
• Designed eval harnesses with labelled sets and regression suites.
• Strong SQL, Python, dbt, and Snowflake expertise.

Responsibilities

• Architect LLM-in-the-pipeline patterns for extraction, enrichment, and validation.
• Design eval harnesses, scorers, and regression suites for model changes.
• Define rule-vs-LLM standards and codify them for the team.
• Manage cost, drift, and token budgets across pipelines.
• Ensure observability with detailed logging of LLM calls and costs.
• Oversee warehouse and transformation layers in dbt, Snowflake, and data quality.

Skills

Production data platforms

Snowflake

SQL

Python

dbt

Airflow

AWS

LLM in pipelines

Architecture judgement

Cost optimization

Tools

dbt Cloud

Snowflake

Airflow

Job description

Firmable is an AI-native organisation building a market-leading data platform. We seek a Senior Data Engineer — Data Platform to 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 in production.

You’ll own LLM-in-the-pipeline architecture, eval harnesses, the rule-vs-LLM standard, cost and drift controls, and observability across the data stack.