Data Engineer

Civic Marketplace is a modern, data-driven platform helping government agencies discover, evaluate, and engage suppliers while improving access to public-sector work for businesses. The Data Engineer will be the first dedicated data engineering hire, owning trusted procurement data, ingestion pipelines, canonical modeling, data reliability, and agentic data infrastructure for AI-powered procurement workflows.

Responsibilities:

• Trust in the data. The north star. A golden, trusted dataset that powers Civic Marketplace's analytics, so when someone asks how many quotes were awarded last quarter, or how much GMV we've captured, there's one number, and it's right. You'd own the diagnosis and the fix
• Ingestion. Pipelines that pull supplier, agency, solicitation and contract data out of sources never designed to be read by anything but a person. Reliable, observable, and cheap enough to add the next source without a meeting about whether it's worth it
• The canonical model. One supplier, one record, across every spelling, trading name, subsidiary and registry identifier. One shared definition of a contract vehicle, a commodity, an agency, that product, sales and customer success all use and none of them argue with. This is the unglamorous part that makes everything downstream possible
• The system underneath. Testing, lineage and freshness, so when the platform tells an agency something we know where it came from and when. And self-serve data for product, customer success and analytics, so nobody queues behind an engineer for a number. Everything you build should work for the next source without you in the loop. This is where AI earns its place, turning what is currently hand-checked into something repeatable
• Agentic data infrastructure. As we scale our use of AI-native procurement, you will own the data layer that powers our agentic workflows. This goes beyond standard retrieval. You will design ingestion and storage strategies, including optimizing vector pipelines like Pinecone for RAG, to ensure our agents have the high-fidelity, context-aware data necessary to perform reliably in production
• You'd work closely with product engineering, customer success and our Growth Lead. Application feature work stays with the product engineering team, so you're not competing for that ground. You own the layer they build on top

Requirements:

• Data engineering experience in a startup or scale-up, where you've built the platform rather than inherited one
• Strong SQL and Python, and the judgement to know when the warehouse is the wrong place to solve a problem
• A data model you designed that other people had to live with, including the parts you would do differently now
• Real experience of messy external sources: half-documented APIs, flat-file drops, scraped pages, PDFs, and data you neither control nor can correct
• Entity resolution or record linkage experience, or clear evidence you'd be good at it. This is the centre of the job, not an edge case
• A commercial head. You can tell the difference between a data problem that's blocking revenue and one that's merely interesting, and you sequence accordingly
• The instinct to find out why a number is wrong before proposing a fix. Sometimes it's the pipeline, sometimes the model, sometimes the source was always like that, and those need different answers
• The instinct to build systems that scale rather than pipelines that run once
• Genuine AI fluency, in the sense above
• The determination to stay positive through the ups and downs of a fast-moving startup, and to find a path through problems rather than wait for conditions to be right
• Curiosity about why public sector data is different. You don't need to have done it. You do need to want to understand it
• Govtech, public sector or civic tech background, or experience working with public records and open data
• Familiarity with supplier and procurement data: SAM.gov and UEI, DUNS, NAICS or UNSPSC, cooperative purchasing, COG or NIGP
• Experience with search and retrieval, whether OpenSearch, Elasticsearch or vector pipelines (like Pinecone or Milvus) feeding an LLM product
• Bilingual English and Spanish, especially valuable for supplier data quality and onboarding
• Experience being the first data hire on a team that had been doing it themselves