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About This Role
Senior Data Engineer (AI\-Native) — Data Layer
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### 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:
- 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.
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.
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 3,708 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Proton.ai, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $178,800 based on 40 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Proton.ai AI Hiring
Proton.ai has 1 open AI role right now. They're hiring across Data Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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