Context

We are Sustaain, a data-for-sustainability startup building a sustainability intelligence platform for complex supply chains such as coffee and cocoa with an initial focus on data management for European Union Deforestation Regulation (EUDR) compliance and risk management.

Over the next 6 months, we’re shipping production-grade compliance workflows: portal UX + APIs, traceability + geospatial services, auditability, and document ingestion & automation as a first-class module. This sits at the heart of the unified compliance operations vision we’re building with large commodity traders.

Role Overview

We are looking for a Data Engineer to industrialize how we ingest, reconcile, and serve compliance-grade data from messy source systems (APIs, files, exports) into a canonical model that powers the portal, the services, and the regulatory outputs.

You will report to our Lead Data & Delivery Lead (already in the team) and work closely with the engineering team and delivery team to bring data services into robust production systems: validation, monitoring, security, performance, and tight integration with product workflows.

Responsibilities

• Build and ship production-grade data ingestion pipelines (API, file-based, hybrid) for traceability, supplier, and geospatial inputs (contracts, deliveries, aggregations, polygons, evidence).
• Define and maintain canonical data models, reference/master data, and ownership boundaries; handle entity resolution and reconciliation across sources.
• Own data validation and error handling: consistency rules, quarantines, retries, lineage, and reconciliation reports that reviewers can trust.
• Implement production guardrails: access control, PII handling, audit trails, and traceability of data changes (who/what/when/why).
• Optimize for reliability and performance (orchestration, backfills, idempotency, partitioning, indexing, cost control).
• Collaborate with product and data teams to turn compliance requirements into “shippable” workflows (review screens, evidence packs, audit trails, operational dashboards).

Required Skills & Experience

• Strong data engineering fundamentals (Python + SQL), production mindset, and a track record of shipping.
• Hands-on experience building pipelines and data services in production (batch and/or event-driven), including testing and observability.
• Solid understanding of data modeling, validation frameworks, and data quality monitoring.
• Comfort with ambiguity: you can define the problem, measure it, and iterate fast.
• You care about correctness and accountability (this is compliance, not “best effort”).

Nice-to-Have