Expert Data Platform Engineer
RemoteStaffEMEAArtificial IntelligenceData AnalyticsPlatform EngineerSoftware EngineerKafkaCoachingReportingTerraformSQL
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Requirements
- Several years of experience as a software engineer or platform engineer working with large-scale data systems, streaming architectures, or data/AI platforms.
- software engineer or platform engineer
- Proven experience designing and delivering data platforms, including real-time ingestion, enrichment, data warehousing, and lake/lakehouse architectures.
- data platforms
- Expertise with streaming technologies (Kafka or similar), reverse-ETL, and modern big-data tooling.
- streaming technologies
- Hands-on experience with platform components such as Terraform, Terragrunt, CI/CD pipelines, Python, SQL, and cloud-native infrastructure.
- Terraform, Terragrunt, CI/CD pipelines, Python, SQL
- 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.
- B2B SaaS or product-led
- 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.
- AI/ML observability
- Experience with event-driven systems and data contracts.
- Typeform drives hundreds of millions of interactions each year, enabling conversational, human-centered experiences across the globe. We move as one team, empowering our collective efforts by valuing each individual’s unique perspective. This fosters strong bonds grounded in respect, transparency, and trust. We champion our diverse customer base by anticipating their needs and addressing their challenges with priority. Committed to excellence, we hold high expectations for ourselves and each other, continuously striving to deliver exceptional results.
- move as one team
- strong bonds
- champion our diverse customer base
- hold high expectations
- We are proud to be an equal-opportunity employer. We celebrate diversity and stand firmly against discrimination and harassment of any kind—whether based on race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or expression, or veteran status. Everyone is welcome here.
Responsibilities
- 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.
- 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)
- Arize AI
- LLM Gateway (model routing, policies, cost controls)
- LLM Gateway
- Pinecone (vector database)
- Pinecone
- Model access APIs
- Model access APIs
- OTEL collector for trace processing
- OTEL collector
- 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.
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