Senior Engineer – Data

orbital; Hyderabad; On-site; INR 2,000,000 - 4,500,000; Full time

Job summary

Fairground is seeking a Senior Data Engineer to own ingestion, enrichment, and quality at scale. You will design AI-powered data systems using LLMs and embeddings, and lead the transformation of messy sources into structured records.

You will model companies and contacts, ensure seamless syncing between the data warehouse, CRM, and products, and drive cost-conscious performance improvements in AI infrastructure while mentoring teammates.

Qualifications

• 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 — production systems with LLMs, embeddings, and vector search
• Experience with data matching, enrichment, or entity resolution
• Comfortable with cloud infrastructure and cost/performance trade-offs
• Able to work independently on ambiguous problems and mentor others

Responsibilities

• Own large-scale data ingestion — pulling and processing millions of records from web sources and third-party providers
• Build AI-powered data systems: use LLMs and embeddings for extraction, classification, entity resolution, and enrichment
• Design and own the enrichment layer — stitching data from multiple vendors into a single trusted record
• Design and evolve the data models and schemas representing companies, contacts, and relationships
• Own the sync between our warehouse, CRM, and products
• Build data quality and feedback loops to catch bad data before customers see it
• Own the cost and performance of AI/enrichment infrastructure — model choice, batching, caching, and spend

Skills

SQL

Python

LLMs

Embeddings

Vector search

ETL pipelines

Data modeling

Tools

Cloud data warehouse

Data ingestion tools

Entity resolution tooling

Job description

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 expand 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.