Senior Data Engineer (LATAM based)

Role overview Build the trusted, governed data foundation that business teams, applications, and AI systems rely on. You’ll reconcile information from multiple sources, turn business definitions into traceable data products, and ensure each consumer can access only the information they’re authorized to see. The role combines hands-on engineering with independent work alongside business stakeholders and a client’s data team.

Responsibilities - Build and maintain ingestion and transformation pipelines, with incremental updates and freshness targets. - Model entities and facts across systems, preserving source, lineage, history, and validity periods; make conflicting definitions visible and resolve them with stakeholders. - Convert agreed business definitions into certified tables and reproducible finance metrics. - Apply access controls to governed data and carry permissions through derived data, APIs, and other interfaces; maintain isolation tests and access logs. - Build data APIs, webhooks, or MCP tools that return deterministic results while enforcing each caller’s identity and permissions. - Establish pipeline testing, data quality monitoring, cost visibility, documentation, and handover practices; use AI coding tools with careful review.

Requirements - At least six years of professional data engineering experience building production pipelines and data models. - At least three years using Databricks in production, including Unity Catalog. - Strong SQL and Python skills; ability to write, debug, optimize, and refactor pipelines independently. - Experience with history-aware modeling, provenance and lineage, cross-system entity reconciliation, and incremental or change-driven processing. - Practical production experience implementing data access controls, plus a clear understanding of where platform controls need application-level enforcement. - Experience with data testing and CI/CD, and with building APIs or services that serve data. - Independent judgment, comfort challenging unclear definitions, daily experience with AI coding tools, and C1 English for direct client collaboration in a remote setting.

Nice to have - Spark or PySpark; multi-tenant data isolation and security testing; finance or CRM data; integration after acquisitions; governed documents or other unstructured content; shared metrics or semantic layers; or data platform cost attribution.

Benefits and work setup Remote work from anywhere, a monthly USD service fee, 20 business days of paid time off plus six local holidays, and caregiver time off.