Senior Data Engineer (AI-Native, Data Layer)

Role overview

Join Proton as a Senior Data Engineer to own the Data Layer, the foundation for Proton’s products and AI. You’ll design end-to-end architecture, build ingestion and transformation pipelines, and ensure data correctness for both operational and analytical access.

Responsibilities

• Build and develop the data layer: ingestion, transformation, and data modeling.
• Construct and operate ingestion + transformation pipelines (modern orchestration framework) feeding the data layer.
• Collaborate with Backend, AI, and Product teams to define data contracts they rely on.
• Ensure data correctness and reliability across failures such as partial loads, late/out-of-order events, idempotency, backfills, and schema drift.
• Use agentic development tools daily to ship real work (e.g., Claude Code, Cursor agent mode, Codex, or equivalents).

Requirements

• Strong programming and SQL skills; can design efficient pipelines, schemas, queries, and models for transactional + analytical patterns.
• Hands-on ELT/ingestion orchestration experience with messy upstream sources.
• Deep understanding of data-consistency failure modes (idempotency, backfills, schema drift, late/out-of-order data).
• English at C1+.
• Cloud data warehouse experience on a major cloud platform.
• Experience ingesting multiple source types: file-based, event/streaming, and API-based.
• Strong debugging ability: read query plans, diagnose slow/expensive pipelines, and root-cause data correctness issues.
• Ownership mindset: take data systems from idea to production with strong judgment.
• 7+ years hands-on data engineering with real production ownership.

Nice-to-haves

• Streaming/event ingestion at scale.
• Medallion or lakehouse architecture.
• Experience with enterprise sources (e.g., ERP like Epicor Eclipse, Prophet 21; ecommerce) and reconciling messy transactional data.
• Building feature/retrieval/serving layers and data contracts for AI/ML or agentic products.
• Prior experience at an early-stage SaaS startup.

Work mode

• Full remote, with meaningful daytime overlap with the Boston team.