Proton.ai - Senior Data Engineer (AI-Native) — Data Layer
Requirements
• 7+ years hands-on as a data engineer with real, demonstrable production ownership — pipelines and data models serving real users at scale. • Strong fundamentals. You understand what your code and your queries are doing and why. You can read a query plan, reason about a slow or expensive pipeline, and debug a data-correctness bug to its root. • Strong programming and SQL skills. You build efficient pipelines, schemas, and queries, and can model data for both transactional and analytical access patterns. • Hands-on orchestration experience, building reliable ingestion/ELT pipelines against messy upstream sources. • Experience with a cloud data warehouse and a major cloud platform. • Experience ingesting from multiple source types: file-based, event/streaming, and API-based. • file-based, event/streaming, and API-based • Solid grasp of data-consistency failure modes — partial loads, late or out-of-order data, idempotency, backfills, schema drift. • Daily, hands-on use of agentic dev tools (Claude Code, Cursor agent mode, Codex, or equivalent) to ship real work. You can talk concretely about how you structure prompts, manage context, parallelize agents, and verify their output. • Ownership and judgment. You take data systems from idea to production and exercise good taste on what to build and what to cut. • Startup mindset and strong communication — pragmatic, fast, biased to ship, and able to explain data decisions to engineers, PMs, and customers in writing. • English at C1 or above. • Big Plus • Deep cloud data warehouse experience and modern transformation tooling. • Streaming / event ingestion at scale. • Medallion or lakehouse architecture experience on large, multi-source data. • Experience integrating enterprise sources such as ERP (Epicor Eclipse, Prophet 21) or ecommerce systems, and reconciling messy transactional data. • Building data systems that feed AI/ML or agentic products — serving/feature layers, retrieval, or data contracts for model inputs. • Prior experience at an early-stage SaaS startup.
Responsibilities
• Own the Data Layer end to end: ingestion from file-, event-, and API-based sources; the medallion-style model (raw → refined → curated); and the serving layer that powers the product and the AI brain. • Build and operate the ingestion and transformation pipelines that power the Data Layer, using a modern orchestration framework and cloud data warehouse. • Ingest and reconcile large, messy, real-world data across many source types and shapes — batch files, streaming events, and APIs. • Model data across medallion layers so it's trustworthy, queryable, and stable for downstream teams and the AI. • Help take the Data Layer to the next level — better architecture, better tooling, more scale, more sources — and have a real say in what that looks like. • Operate AI coding agents (Claude Code and similar) at a high level: scope work, structure context, run agents in parallel where it makes sense, and ship reviewed, production-quality output. • Build the systems that make data trustworthy — validation, reconciliation, lineage, backfills, idempotent and incremental loads — so downstream teams and the AI don't inherit silent errors. • Partner with backend, AI, and product engineers (and occasionally customers' IT teams) to define the data contracts they build on.
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