Expert Data Platform Engineer

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Typeform is a refreshingly different form builder. We help over 150,000 businesses collect the data they need with forms, surveys, and quizzes that people enjoy. Designed to look striking and feel effortless to fill out, Typeform drives 500 million responses every year—and integrates with essential tools like Slack, Zapier, and Hubspot. Typeform is fully remote by design. For this role, we can hire candidates based in the UK, Ireland, Germany, Spain, Portugal, or the Netherlands.

We’re the team that builds and maintains the data pipelines, tracking systems, and platform components that power Typeform’s Data & AI ecosystem. We partner closely with Data Science, Analytics, AI Engineering, Platform Engineering, and IT to deliver trusted, scalable, near–real-time data assets and AI‑ready infrastructure. Today, we’re early in the journey: processes are fragmented, ownership is shared across several teams, and we’re modernizing our stack to support real‑time AI products.

Your work will be foundational in bringing clarity, governance, and engineering excellence to this space.

We’re looking for an Expert Data Platform Engineer to bring deep technical leadership to our Data Platform and Tracking teams. You’ll shape the next generation of our data and AI platform architecture, introduce modern governance, and take ownership of the mission‑critical components that power our AI strategy. You’ll report to the Senior Data Platform Manager and work closely with leaders across Platform, Data Science, AI Engineering, and Tech Ops.

While you won’t directly manage people, you will influence engineers across multiple teams and raise the technical bar through mentorship, architecture, and hands‑on implementation. This role is central to enabling Typeform’s AI ambitions. You’ll translate product and AI requirements into platform capabilities, consolidate fragmented tooling, build for scale, and ensure our real‑time and AI infrastructure is stable, observable, cost‑efficient, and ready for what’s next.

• Serve as the technical authority for data platform and tracking architecture—guiding design decisions, tool selection, and engineering best practices.
• Design a next‑generation data & AI platform that supports near‑real‑time ingestion, enrichment, and model‑driven product features.
• Translate AI Engineering requirements into clear specifications and capability roadmaps through spec‑driven development.
• Define and implement modern data governance: data quality, lineage, ownership, observability, and cost visibility.
• Evaluate, consolidate, or replace redundant technologies, introducing scalable SaaS solutions where appropriate.
• Own and extend core AI‑platform components, including:
  • Arize AI (evals & observability)
  • LLM Gateway (model routing, policies, cost controls)
  • Pinecone (vector database)
  • Model access APIs
  • OTEL collector for trace processing

• Take full ownership of the AI Infrastructure components currently run by AI Engineering—ensuring reliability, scale, and clear fallback strategies.
• Document existing workflows across Analytics Engineering, Data Engineering, and AI Engineering to identify automation and process improvements.
• Introduce streamlined development pathways for AI—templates for CI/CD, Git Ops, Python microservices, and AI‑specific libraries.
• Implement Fin Ops principles: baseline costs, identify inefficiencies, and reduce spend across data and AI workloads.
• Support the transition toward a more self‑serve model through ADRs, RFCs, and well‑defined ownership guidelines.

• Mentor Data Engineers on modern Data Platform concepts—streaming, governance, observability, platform reliability, and AI‑aligned workflows.
• Partner with Platform Engineering, Tech Ops, Data Science, and AI Engineering to drive alignment and cohesive infrastructure strategy.
• Define playbooks and guidelines that standardize how teams build pipelines, collaborate with Platform, and deliver AI‑ready data.
• Increase trust across…