Senior Data Engineer (AI-Native) — Data Layer

Proton.ai; United States; Remote; USD 134,000 - 202,000; Full time

Job summary

Proton.ai is hiring a Senior Data Engineer to own and grow the Data Layer—the unified foundation powering our product and AI brain. You’ll design end-to-end ingestion, modeling, and serving pipelines across raw, refined, and curated medallion layers.

You’ll work with Claude Code and other agentic tools, ensuring data correctness, implementing validation, lineage, and backfills, while collaborating with backend, AI, and product teams.

Qualifications

• 7+ years hands-on data engineer with production ownership.
• Strong SQL and programming skills for pipelines and data models.
• Experience with cloud data warehouses and orchestration frameworks.
• Experience ingesting from multiple sources: file-based, event/streaming, and API-based.
• English at C1 or above.

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.

Skills

Data engineering

SQL proficiency

Programming

Data modeling

ELT pipelines

Cloud platforms

Agentic tools

Tools

Airflow

Claude Code

BigQuery

Job description

Senior Data Engineer (AI-Native) — Data Layer

About Us

Proton is building the AI operating system for wholesale distribution, embedded in the workflows that move nearly every physical product on the planet. Distribution is a $9 trillion industry, and the software that runs it has been stuck in the past for decades — most tools create more work than they eliminate. We unify CRM, PIM, eCommerce AI, and Order & Quote Entry AI into one platform with one data layer and one AI brain, so reps spend their time deepening customer relationships, not entering data.

We hire people with high agency and high urgency. At Proton, everyone is a builder who owns problems end to end and ships AI-native software faster than anyone else in the category.

The median Proton customer reports 3x profit return per dollar spent, an extra day of sales per rep per month, and “the best ROI of any tech investment in 30 years.” Hundreds of leading distributors — from family-owned shops to publicly-traded enterprises across, industrial, HVAC, electrical — run on Proton. We’re backed by Felicis Ventures (Twitch, Shopify, Opendoor) and Battery Ventures.

If you want to build the systems that shape how trillions of dollars of physical goods move through the economy, Proton is the place to do it.

The Role

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:

1. 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.
2. 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.

Location: Europe, remote. Meaningful daytime overlap with our Boston (EST) team required.

What You’ll Do

• 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.

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.
• 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.