pWe're building the future of mobility. /ppAt hlpy, we're building much more than a company. /ppWe're building the technology platform that is transforming how mobility services are delivered across Europe. /ppEvery month, our platform orchestrates tens of thousands of real-world operations, connecting drivers, insurers, fleets, OEMs, service providers and business partners through technology. /ppAs we continue to expand internationally, broaden our product offering and integrate new businesses, the complexity of what we do grows with us. /ppScaling hlpy isn't simply about increasing revenues. /ppIt's about building an organization capable of executing consistently across countries, products and teams. /ppTo get there, strategy alone isn't enough. /ppExecution is what makes the difference. /ppThat's why we're looking for a genuinely senior, hands-on Data Engineer to join our AI Data team as a peer to our current engineers — someone who owns definitions, not just deliverables. /ppOur analytics platform — dbt on Snowflake, fed by CDC replication from our operational PostgreSQL estate, by our ERP, and by partner files and webhooks — is what tells hlpy and its enterprise customers what actually happened on every roadside assistance mission across four countries. Those numbers are not decoration: they settle SLAs, they drive invoices, and they land in our customers' inboxes every morning. /ppYou will own models end to end, from raw source to the extraction files our contracts depend on, and you will be expected to push back when a number is wrong. /ppbr / /ppstrongKey Responsibilities /strong /ppstrong1. Modelling Ownership (dbt on Snowflake) /strong /pulliDesign, build and own models across the full stack — staging, intermediate, the entity-pure silver layer, marts, and the customer-facing outbound extractions — in a dbt project of roughly 200 models and growing. /liliBuild models that stand up to scrutiny. There are cases where the same business event is captured by more than one system, and the right answer is not obvious from the data alone: we expect you to investigate, connect with the people who produce and use that data, agree on a rule, publish it with its provenance, and make it testable. /liliTake genuine end-to-end responsibility: scoping, modelling, testing, review, release — and what happens in production the morning after. /liliMigrate legacy business logic out of hand-written database views and ad-hoc scripts into dbt, without silently changing a number a customer is already reading. /li /ulpstrong2. Semantics, Data Quality Governance /strong /pulliTreat definitions as a deliverable. A metric is not done until it is named, documented, tested,



and impossible to compute a second way somewhere else in the estate. /liliBuild the test layer that catches real defects — grain, referential integrity, reconciliation against source, negative durations, silent timezone reinterpretation — rather than decorative tests that always pass. /liliMeasure the impact of every definition change before it ships, and communicate it clearly to the teams that rely on the number. /liliHelp maintain and extend our house conventions, which live in the repository, are graded by a linter in CI, and are changed by merge request like any other code. /liliContribute to the shared vocabulary: one name, one meaning, written down, for every event and metric the business argues about. /li /ulpstrong3. Pipelines, Sources Platform /strong /pulliWork hands-on with the ingestion layer that feeds the warehouse: CDC replication from PostgreSQL (Airbyte), API extraction from NetSuite via SuiteQL, partner file drops over S3 and SFTP, and inbound webhooks. /liliKeep it healthy and affordable: replication slot health and WAL budgets, incremental strategies, warehouse and connector cost, and the honest trade-offs between CDC and cheaper capture methods. /liliContribute to our orchestration migration (Airflow to Astronomer) and to retiring the legacy reporting jobs that still live outside dbt. /li /ulpstrong4. Customer-Facing Data Products /strong /pulliBuild and operate the daily extractions, SLA reports and KPI files that our enterprise fleet, leasing and rental customers receive — contractual deliverables with a recipient, not dashboards. /liliModel contractual logic — thresholds, exclusions, effective dates, per-partner exceptions — so it lives in one governed place instead of in a folder of spreadsheets. /liliWork directly with other business units and the customer-facing teams to agree what a number means before building it, and to explain it afterwards. /li /ulpbr / /ppstrongWhat This Role Is Not /strong /pulliNot a dashboard-building or BI-reporting role. /liliNot a pure ingestion or plumbing role — the modelling and the semantics are the job. /liliNot a ticket-taking role: we are not looking for someone who writes SQL to someone else's spec. /liliNot a data science or ML role.



/li /ulpbr / /ppstrongRequired Skills Experience /strong /ppemThis is a real senior role. We are hiring for judgement as much as for tooling. /em /pulli5+ years building and operating production data platforms, with several of those years spent doing analytics engineering on a modern cloud warehouse. /liliDeep, hands-on dbt: incremental models, snapshots and SCD2, macros, generic and singular tests, packages, selectors, exposures, CI runs — plus firm opinions on project structure and layering, and the ability to defend them. /liliExpert SQL on a cloud warehouse (Snowflake strongly preferred): window functions, query profiling, clustering, and cost and performance tuning. /liliComfortable with an orchestrator (Airflow, Astronomer, Dagster or similar) and with Git-based delivery: branches, merge requests, peer review, CI. /liliReliable on commitments: you plan realistically, flag risks early, and hit the deadlines you agree to — our customer-facing deliverables run on a daily schedule and cannot slip. /liliFluent working alongside AI coding agents as part of your day-to-day delivery. /liliExperience modelling from source systems you do not control, where the upstream data is incomplete, contradictory or occasionally wrong. /liliThe seniority signal we care about most: you can take a vague business question, investigate how the data is actually produced upstream, and come back with a model, a number, and the caveats that belong with it. /liliProfessional working English, written and spoken. Italian is welcome but not required. /li /ulpbr / /ppstrongNice to Have /strong /pulliExperience modelling ERP, billing or invoicing data (NetSuite a strong plus). /liliSemantic layer or metrics layer experience. /liliExperience with contractual, regulated or externally audited reporting, where a wrong number has consequences. /liliData contracts, lineage or data observability tooling. /li /ulpbr / /ppstrongWhat We Offer /strong /pullistrongSalary package /strong ranging from € 55.000 to € 60.000 strongand MBO. /strong /liliFresh fruit and good snacks to share with your nice colleagues /liliTraining budget /liliRemote Working /li /ulpbr / /ppstrongWhy hlpy /strong /ppBecause we're building something meaningful. /ppBecause we move fast. /ppBecause responsibility grows faster than hierarchy. /ppBecause we believe the best ideas can come from anyone. /ppBecause we value ownership over politics and execution over bureaucracy. /ppAnd because if we do our job well, millions of people across Europe will experience mobility in a smarter, faster and more reliable way. /ppstrongIf building rather than maintaining excites you, we'd love to meet you. /strong /ppbr / /p

📌 Senior Data Engineer (Analytics Engineering) (Roma)
🏢 HLPY
📍 Roma