Senior Data Engineer - São Paulo (Contract)

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.

• →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.
• →AI-assisted engineering. We use AI agents in day-to-day development, and you'll apply them where they add leverage, with guardrails and traceability.
• →

• Strong SQL and Python, applied across the full data lifecycle from ingestion to serving.
• Solid, hands-on Databricks experience: notebooks, jobs, workflows, Delta Lake and PySpark in production. It's our core platform and we won't be teaching it.
• Data modeling depth: dimensional modeling, slowly changing dimensions, grain design, medallion or equivalent layered architecture.
• Entity resolution and data reconciliation. This is the core of the role.
• Data quality and observability: testing, monitoring, alerting, lineage.
• Transformation and orchestration frameworks (dbt, Airflow, Databricks Workflows or equivalent), with version control and CI/CD applied to data models.
• Experience with data products feeding a live application, not only dashboards, and the ability to translate an ambiguous business question into an actionable number.

• Data product architecture. We run multiple data products (permits, deeds, tax assessor, businesses and contacts), each built from several sources. Experience designing decoupled, product-oriented data architecture will set you apart: how domains map to models, how pipelines are organized and run per product, and how data is laid out and stored in a lakehouse.
• AI architectures applied to data: pipelines for RAG, MCP for standardized data-source access by agents, and quality and traceability in non-deterministic agentic systems.
• Advanced Databricks ecosystem: Unity Catalog, Delta Live Tables.
• Data governance: access control, cataloging, lineage.
• MLOps beyond the basics: model serving, drift monitoring, retraining workflows.