Expert Data Platform Engineer

Typeform is fully remote by design. For this role, we can hire candidates based in the UK, Ireland, Germany, Spain, Portugal, or the Netherlands.

About the Team

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.

About the Role

We’re looking for a Lead 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 TechOps. 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.

What you’ll do

Technical leadership & architecture

• 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

Platform ownership, optimization & governance

• 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, GitOps, Python microservices, and AI-specific libraries.
• Implement FinOps 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.

Mentorship & cross-team collaboration

• Mentor Data Engineers on modern Data Platform concepts—streaming, governance, observability, platform reliability, and AI-aligned workflows.
• Partner with Platform Engineering, TechOps, 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 the organization through clear documentation, architecture records, and consistent communication.

What you bring

• Several years of experience as a software engineer or platform engineer working with large-scale data systems, streaming architectures, or data/AI platforms.
• Proven experience designing and delivering data platforms, including real-time ingestion, enrichment, data warehousing, and lake/lakehouse architectures.
• Expertise with streaming technologies (Kafka or similar), reverse-ETL, and modern big-data tooling.
• Hands-on experience with platform components such as Terraform, Terragrunt, CI/CD pipelines, Python, SQL, and cloud-native infrastructure.
• Experience partnering with Data Science, AI Engineering, and cross-functional teams to translate requirements into scalable platform capabilities.
• Strong communicator with the ability to influence stakeholders and document architecture clearly.
• Experience mentoring, coaching, or leading engineers technically (even without direct reporting lines)

Extra awesome

• Experience building data, ML, or AI platforms in a B2B SaaS or product-led environment.
• Familiarity with cost-optimizing cloud data workloads and data governance frameworks.
• Exposure to AI/ML observability, vector databases, model gateways, or modern AI infra architectures.
• Experience with event-driven systems and data contracts.