Senior Engineer – Data

About Fairground

Fairground is a B2B GTM data platform. We ingest, enrich, and structure millions of company and contact records and turn them into reliable go-to-market signal inside our products. The pipeline behind that data is the hardest and most valuable engineering problem in the company - and increasingly, it's an AI problem.

Why this role exists

Our data pipeline has outgrown the generalist engineers currently keeping it running. We need a senior engineer who lives in the data: someone who can own ingestion, enrichment, matching, and quality at scale - and who reaches for AI/LLMs as a core tool, not a bolt-on. A huge amount of our hardest work (extracting structure from messy data, resolving entities, classifying and enriching records) is now best solved with models, and we need someone fluent enough to build those systems well and cost-effectively.

What you'll do

Own large-scale data ingestion - pulling and processing millions of records from web sources and third-party providers, reliably and efficiently

Build AI-powered data systems: use LLMs and embeddings for extraction, classification, entity resolution, and enrichment where traditional rules break down

Design and own the enrichment layer - stitching data from multiple vendors into a single trusted record (matching logic, dedup)

Design and evolve the data models and schemas that represent companies, contacts, and their relationships as we into new data domains

Own the sync between our warehouse, CRM, and products

Build the data quality and feedback loops that catch bad data before customers see it

Own the cost and performance of our AI/enrichment infrastructure - model choice, batching, caching, and spend are real engineering decisions here

What we're looking for

5-8 years of data engineering experience, owning systems end to end

Strong SQL and Python, and hands-on experience with a modern cloud data warehouse

Production experience building ingestion/ETL pipelines at scale

Real AI fluency - this is core to the role, not optional. You've built production systems with LLMs (structured extraction, classification, RAG, or agentic pipelines), understand embeddings and vector search, and know how to make models reliable and cost-effective at scale. You have a point of view on when to use a model vs. when not to

Experience with data matching, enrichment, or entity resolution - turning messy multi-source data into clean records

Comfortable with cloud infrastructure and mindful of cost/performance trade-offs

Able to work independently on ambiguous problems and mentor others

Why Fairground

You'll own the data layer at a company where data is the entire value proposition - and you'll get to build it on the frontier, using AI to solve problems that were impossible a couple of years ago. High ownership, direct customer impact, fast-moving team.