Data Engineer

Martelo turns public records into commercial intelligence for the U.S. construction industry. We ingest building permits, deeds, tax assessor records and property data from sources across the country, resolve the conflicts between them, and deliver clean, connected data products that manufacturers, distributors and contractors use to find their next customer.

We started by transforming messy, fragmented property and permit data into clear, actionable insight. That foundation is now live and in customers' hands, and here the data is the product: the quality of our entity resolution, our reconciliation logic and our data models is what our customers pay for.

We are still a small, ambitious team that thrives on curiosity, resilience and purpose. We are builders, crafting technology from the ground up, exploring, experimenting and learning fast. The journey has moved past its first chapter, but the vision is unchanged: to build the leading data-intelligence platform for construction.

Your Title: Senior Data Engineer.

Your Location: Hybrid/Brazil HQ address - Nações Unidas 12901 - São Paulo - SP (the team meets at the office once in a while.).

About the Opportunity:

Senior individual contributor with end-to-end ownership. You gather requirements with go-to-market and product, design the solution, build it, prove it's correct, and keep it healthy in production. You lead the execution of your own work, and we expect you to propose work to the roadmap when you spot it before we do.

Our platform runs on Databricks and you'll be working on it daily from week one. We are not set up to ramp someone on the fundamentals.

What you will do:

• Entity resolution and master data management: source-of-truth rules, survivorship logic, stable identifiers and manual overrides across permits, deeds, assessor records and business/contact data. This is the heart of the role.
• Reconciliation of third-party sources that disagree, at national scale, plus the observability that tells us when the logic drifts.
• Model stewardship: land new sources inside our production data models, and evolve those models when they no longer fit instead of bolting on workarounds.
• Data quality end to end: testing, monitoring, alerting, lineage, and root cause analysis when incidents happen.
• Analysis that shapes the roadmap: which sources to acquire next, which datasets to prioritize, and what quality gaps actually cost us commercially.
• Productionizing data science models: the data models, contracts and drift monitoring around ML.
• Technical reference for the team: code review focused on correctness, cost and maintainability trade-offs.