Staff Data Engineer

About Artisan We're building AI employees. Not chatbots. Not copilots. Autonomous digital workers that do real jobs. Our first, Ava, is an AI BDR used by hundreds of companies. She researches leads, writes and sends emails in a customer's voice, runs multi-step outbound sequences, manages her own deliverability infrastructure, self-optimizes over time, and handles objections and meeting booking. She's not a tool someone uses. She's a teammate. We're a YC W24 company, have raised $35M+ from investors including Y Combinator, and are at $9M+ ARR. We just launched Ava 2.0, a step change in what an AI employee can do. The engineering problems are hard and the surface area is enormous. Role overview You'll be the first Data Engineer on the Artisan team! We're managing a database of hundreds of millions of leads and creating real-time intent signals which monitor data fields for those leads. You'll own everything data-related at Artisan. Design, build, and maintain scalable data pipelines that process and transform large volumes of structured and unstructured data Manage ingestion from third-party APIs, internal systems, and customer datasets Develop and maintain data models, data schemas, and storage systems optimized for ML and product performance Collaborate with ML engineers to prepare model-ready datasets, embeddings, feature stores, and evaluation data Implement data quality monitoring, validation, and observability Work closely with product engineers to support new features that rely on complex data flows Optimize systems for performance, cost, and reliability Contribute to early architecture decisions, infrastructure design, and best practices for data governance Build tooling that enables the entire team to access clean, well-structured data Location: San Francisco, New York, or Remote USA Team: Engineering Reports to: CPTO, Sam Stallings Who you are 3+ years of experience as a Data Engineer Proficiency in Python, SQL, and modern data tooling (dbt, Airflow, Dagster, or similar) Comfort working in fast, ambiguous environments Experience designing and operating ETL/ELT pipelines in production Experience with cloud platforms (AWS, GCP, or Azure) Familiarity with data lakes, warehouses, and vector databases Experience integrating APIs and working with semi-structured data (JSON, logs, event streams) Strong understanding of data modeling and optimization Bonus: experience supporting LLMs, embeddings, or ML training pipelines Bonus: startup experience Interview process Introductory chat with our recruiter 1 hour technical interview with an engineer 1 hour technical interview with an engineer 30-minute interview with Sam, our CPTO 15-minute culture and values interview with Jaspar, our CEO Our culture and values Founder mindset. Everyone acts like an owner: take initiative, think big, challenge ideas, and push for 10× outcomes Obsessed with impact. We apply the 80/20 rule, kill sunk costs quickly, and focus on what actually moves the needle Customer-first, always. Every decision is made with the customer experience at the center High standards, every detail. Quality matters in everything we ship, from product and code to copy and design Clear, direct communication. We value candor, fast responses, and feedback Winning team energy. We bring positive vibes, low ego, zero drama, and genuinely enjoy building together