Senior Data AI engineer

About the role We are looking for a Senior Data/AI Engineer to lead client engagements end to end. You will understand the business problem, design the architecture, and ship the pipelines and models the client actually runs on. You will work across different clients and industries with the best of the modern data and AI stack. This is a lead-from-the-front role. You own the client relationship and the delivery for your engagements. Having said that for the first 6 months you will work under a lead and move to manage your own projects when you have demonstrated that you are ready. We pay for outcomes, not hours, so speed and quality are the job. We use AI agents and accelerators across our whole delivery workflow, and we expect you to work the same way. We hire data engineers because data foundations are the core of almost everything in AI. We expect our engineers to go deep, with AI agents and with software. We don't expect everyone to have every skill on day one. That is why these three things matter more to us than any single tool on your resume: 1.

Proactive. You don't wait for permission. You see what needs doing and you start. 2.

Growth mindset. Most weeks you'll do something you haven't done before. You get started quickly, figure it out, and don't wait until you feel ready. 3.

Technical. Strong fundamentals beat a long list of tools. You can figure it out, even the first time you're seeing a language or a system. What you will d oA few engagements to show the shape of the work

• :Healthcare : A healthcare company needs to move tens of thousands of patient intakes through the US healthcare system, faster and with fewer errors. You build the data foundation and the AI workflow that lets intake scale, and you stay on as volume grows
• .Hedge fund : A hedge fund wants to keep running the models its team relies on, while building solid data foundations underneath so it can build agentic systems on top. You design those foundations without disrupting how the team already works
• .Insurance : An insurer wants to open entirely new states with an AI-first process, in one of the most heavily regulated industries there is. You build the agents that makes expansion repeatable and keeps it compliant .Across engagements, you will
• :Embed with client teams to understand their business and data, then ship the right solution, not the fanciest one
• .Build and maintain data pipelines, ETL/ELT processes, and warehouse models, plus the semantic + conversational AI layers on top
• .Design data models that power client reporting and decisions
• .Build real AI, not just pipelines. The data engineering is the foundation; on top of it you'll do serious AI work, from agents and RAG systems to AI-native workflows running in production
• .Work AI-first. Use AI to generate and review code, automate data validation and quality checks, write documentation, and move faster, then bring those gains to your clients
• .Own the client relationship for your work, with proactive updates, real scoping conversations, and risks surfaced early
• .Stay current with the data and AI ecosystem and fold what works back into how we deliver . What we are looking f
• or5-10 years in data engineering, with a track record of delivering end to end in productio
• n.Strong SQL and Pytho
• n.Core data engineering stack, the basics: warehouses (Snowflake, BigQuery, Databricks), modeling (dbt), orchestration (Airflow or Dagster), and a semantic layer (Cube or dbt). This is table stake
• s.Agentic stack, where we go deep: LLM APIs (Claude, OpenAI), agent frameworks and protocols (LangGraph, MCP), AI coding tools (Claude Code, Cursor, Codex), and RAG with vector stores (pgvector, Pinecone). This is newer and moves fast, so we care that you can pick it up, not that you've used all of i
• t.You use AI tools every day in how you build, and you have real opinions about where they help and where they fall shor
• t.Bonus: applying AI or ML to data problems like anomaly detection, data quality monitoring, or automated insight
• s.You can talk to a customer with the same ease with which you build dataset
• s.You get things done with minimal supervision. You unblock yourself, you communicate, and you treat the client's outcome as your ow n. How we payWe think paying for time is broken. On a factory line, hours were a fair proxy for output. In our world the best people deliver multiples more than everyone else, and AI widens that gap. So we built comp around what you deliver, not how long you have been here or what your title sa ys.You get a base salary and that is your floor. Beyond a normal month of delivery, you share in the value you create. It is real money, paid every month, and it is why our strongest engineers take home 2 to 3x their base. It is all tracked on a live leaderboard the whole company can see. We are open about it on purpose. The people at the top are not doing anything secret. Watch what they do and copy it. How we
• work100% rem ote. Work from wherever you do your best w
• ork.Outcomes, not ho urs. What ships is what cou
• nts.Transpare ncy. We're transparent about what's happening on the inside and expect you to be the s
• ame.Growth mind set. We're curious and love to learn and experim
• ent. Fun. We love what we do and it feels like the best work of our careers. We expect it to be the same for you.