Senior Data Engineer (AI-Native, Data Layer)

• We’re hiring a Senior Data Engineer to own and grow our Data Layer — the unified foundation that every Proton product and our AI brain are built on
• You’ll own the pipelines and architecture end to end: the medallion-style layers (raw → refined → curated), ingestion from a wide range of systems, and the serving the whole company depends on
• We’re investing heavily to make the Data Layer bigger and better, and this role is for someone who wants to do the hands‑on building and shape where it goes next
• Two things make this role different from a standard data engineering opening:
• You’re an AI-native operator, not a pipeline author.

Every engineer at Proton uses Claude Code and other agentic tools as first‑class collaborators.

We expect pipelines, models, and migrations to be built and shipped with heavy AI leverage — you guide the agents, validate the output, and make the judgment calls they can’t

• You own the layer everything runs on.

When the AI gives a wrong answer or a number doesn’t match the source, the trail leads back to the data.

You own correctness end to end — ingestion, modeling, reconciliation, and the contracts other teams depend on

• Meaningful daytime overlap with our Boston (EST) team required
• 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

Benefits

• Company stock options: We believe everyone should own a part of something they help build, so we offer company equity to all employees.
• Flexible schedule: Work is important to us. As is being flexible. We know life doesn’t always land outside of a 9-5 schedule and make space for you to live your life.
• Unlimited Required PTO: We offer unlimited PTO, and we make sure everyone takes at least 2 weeks off per year.
• Company wellness days: In addition to company holidays, we offer wellness days, when the whole company takes a day off to rest and recuperate.
• 12 weeks paid parental leave: 12 weeks paid parental benefit for all parents (birthing, non-birthing, adopting, fostering).
• Health benefits: Get access to great healthcare benefits.
• 401k with employer contribution: We believe it’s important to help our team plan for the future.

For U.

S. employees (for now), we directly contribute a 3% match of your salary to a 401k.

• Company-paid off-sites: Enjoy in-person brainstorming and team-building. All expenses covered so you can focus on bonding with the entire team.

Requirements

Strong programming and SQL skills.

You build efficient pipelines, schemas, and queries, and can model data for both transactional and analytical access patterns 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 English at C1 or above Hands‑on orchestration experience, building reliable ingestion/ELT pipelines against messy upstream sources Solid grasp of data‑consistency failure modes — partial loads, late or out‑of‑order data, idempotency, backfills, schema drift Startup mindset and strong communication — pragmatic, fast, biased to ship, and able to explain data decisions to engineers, PMs, and customers in writing Experience with a cloud data warehouse and a major cloud platform Ownership and judgment.

You take data systems from idea to production and exercise good taste on what to build and what to cut7+ years hands‑on as a data engineer with real, demonstrable production ownership — pipelines and data models serving real users at scale Experience ingesting from multiple source types: file-based, event/streaming, and API-based 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 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 Saa S startup

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