About the role
Requirements A data leader who's still hands-on. You set strategy and grow
the team, but you stay in the codebase designing schemas, building pipelines and making the architectural calls , Strong at data modelling across messy, federated sources. You take fragmented, conflicting inputs from partners who don't coordinate with you and turn them into one coherent model the whole company builds on , Experienced running real-time streaming pipelines at scale (Kafka, Kinesis, MSK or equivalent). That includes contract management, delivery guarantees, replay, and keeping data correct when upstream sources fail or change format , Deep with PostgreSQL at high volume: partitioning, time-series patterns, query and index tuning, and a clear view on the self-managed versus managed tradeoffs , Able to design the path from operational data to a modern cloud-based analytical platform , Comfortable with transformation tooling like dbt and orchestration like Airflow. You know where a CDC-into-a-replica pattern stops scaling , A machine learning practitioner. You've built and shipped ML on real-world data, from feature engineering through to productionised models, and you can lead a team into ML-driven products , Serious about data quality and reliability. You treat freshness and correctness as first-class SLAs, with real monitoring and lineage in your data ops pipelines , Experienced with data governance and privacy under GDPR, ideally in a regulated or contractual B2B setting , Someone who has built and led data teams. You set priorities and turn strategy into scoped, sequenced projects your engineers can actually deliver , Pragmatic and cost-conscious. You secure the foundations before big rebuilds, work incrementally, and make deliberate build-vs-buy calls , A confident communicator. You can hold a data voice at the exec table, partner cleanly with Engineering, DevOps/Platform and Product, and put data into business terms What the job involves You'll own data end to end at Volteras, an API platform connecting fleet operators to real-time vehicle data and scaling globally in a fast-moving market , You're joining a working platform, not building from scratch. Live ingestion from multiple OEMs runs in production through a managed streaming layer, a unified data model serves a live public API and customer portal, and analytics is already split out via CDC into a separate BI database. Your job is to mature and scale it , You'll set the vision and the multi-quarter roadmap for the platform, make the build-vs-buy calls, and keep it scaling with the business , You'll lead the build of our intelligence layer. Today we move data reliably. Next we make it intelligent, using machine learning to turn raw telemetry into insight customers can act on: a clear read on the health of every vehicle in a fleet, early warning of problems before they get expensive, and concrete ways to capture value and avoid cost. It's probably the biggest open opportunity in the company , You'll own the unified data model and partner data contracts, the ingestion and transformation pipelines, the analytics architecture, schema and query design, data quality, and data governance and privacy , You'll own the reliability standards for the platform (durability, recoverability, availability) along with the cloud cost and capacity trade-offs, working alongside the Head of Platform who runs the infrastructure , You'll own the governance frameworks: data classification, lifecycle and retention, access and consent, and the GDPR/PII obligations that come with sensitive telemetry. Keeping the company audit-ready is on you , You'll enable internal decision-making with solid analytics and BI, while keeping the data that is itself the product coherent and trustworthy , You'll bring a data voice to commercial and exec decisions: company strategy, pricing and packaging, and partnerships , You'll grow and lead the data team, starting with the data engineer already in place
