Senior Data Engineer (m/f/d)

Description As one of the leading German HealthTech companies, we are reshaping discharge management – technology-driven, patient-centered, and free from bureaucracy. In addition to our market-leading SaaS platform, we develop AI solutions that radically simplify processes in hospitals and for aftercare providers, relieve healthcare professionals, and refocus attention on patients. Today, we already connect two-thirds of all German hospitals with over 650 rehabilitation clinics and 25,000 nursing and homecare providers. With currently around 100 employees, we continue to grow – and we are looking for people with character who want to help us improve the healthcare system and solve administrative complexity across care journeys in Europe. Our data flows from our applications, product events and business systems, in a highly regulated healthcare context. This role exists to build the platform that makes sure that data arrives complete, on time and safe to use, so Product, leadership, RevOps and Finance work from the same numbers. You will be the first data engineer in a new data team that sits in Product, works across the company and reports to the Head of Data. You will own the platform that moves our data into Snowflake and shape the architecture decisions around it: which tools we build on, how pipelines are orchestrated and monitored, and how data infrastructure is managed as code. What to expect This is what you can expect from us as a Senior Data Engineer: Purposeful work – your work gives the teams serving hospitals and aftercare providers data they can trust. Real ownership – as the first data engineer in a new team, you shape the data platform and its tooling. Company culture – we believe in flat hierarchies that promote high performance and strong team dynamics. We foster an environment characterized by mutual respect, loyalty, and recognition. Together, we strive for our goals – and expect the same from you. Flexibility – want to pick up your child from daycare? Like to exercise during lunch? We’ll support you. We are a remote-friendly company offering flexible working hours. Workations are also possible by arrangement. Edenred card – which you can use according to your needs. Extra vacation day – so you can celebrate your birthday with your loved ones, you’ll have the day off. This is how you will make an impact as a Senior Data Engineer: Data from our application databases, product events and SaaS tools needs to reach Snowflake reliably. You build and run that ingestion and decide where to buy a managed connector and where to build. Our pipelines need clear dependencies, retries, alerting and SLAs. You set up orchestration so a failed or partial load stops downstream work instead of silently producing wrong numbers. New products need event pipelines. You design them together with engineers, keeping patient data out of analytics events by design. Product and Finance need to work from the same definitions. You maintain the dbt models that connect product usage with customer, billing and revenue data. Our CRM (HubSpot) and our internal systems need to talk to each other. You build the data flows between them, including two-way synchronisation where it is needed. Data quality problems need to be caught before a dashboard or a board report does. You establish validation, monitoring and alerting. Our data needs strong privacy and access controls. You enforce role-based access and masking in Snowflake, pseudonymisation, and deletion that propagates across raw and modelled data. Data code needs the same engineering standards as our product code. You bring code review, testing, documentation and CI/CD to it. How we build We run a modern cloud-native stack to move, model and protect data reliably in a regulated healthcare environment. Cloud: AWS (The European Sovereign Cloud partition) Runtime: Kubernetes (EKS) and Postgres (RDS) Warehouse and modelling: Snowflake (EU), dbt Orchestration: Airflow or Dagster (being introduced) BI: Looker Observability: Datadog CI/CD: GitHub Enterprise Cloud with Actions Infrastructure: CloudFormation Business systems: HubSpot, Chargebee Requirements Several years (typically 5 or more) building production data pipelines, as a data engineer or as a backend engineer on data-intensive systems. Strong Python: you write tested, maintainable production code, not only scripts. Advanced SQL, including window functions, CTEs and query optimisation. Production experience with Snowflake or a comparable cloud warehouse: compute sizing and cost control, role-based access, masking, and performance tuning. Experience with dbt, or a similar tool for tested and documented transformations. Solid ingestion fundamentals: initial versus incremental loads, change data capture from Postgres, idempotency, schema evolution, late and out-of-order data, and backfills. Production experience with an orchestrator such as Airflow or Dagster. Experience with AWS and with deploying your own work in containers. A data quality mindset: you profile data, test assumptions and build checks. Practical knowledge of GDPR: you can identify personal data and implement pseudonymisation, access control and deletion. You use AI coding tools in your daily work and know where they help and where they need checking. Ability to own decisions as the only data engineer and to explain technical trade-offs to non-engineers. Preference for writing over talking, resulting in clear, concise, yet complete documentation and asynchronous communications. Fluent English, written and spoken. Other languages are a plus. Bonus points Healthcare or regulated-domain experience (e.g. ISO 27001, C5). Integrating CRM and SaaS systems such as HubSpot or Salesforce, including reverse ETL or two-way sync. Designing product event tracking (e.g. Snowplow or Segment). Kubernetes in production. Reading and changing Go code. Managing Snowflake as code. German language skills. Our values This will help you decide if you want to join us – our values are: Strong opinions, weakly held. We adapt, negotiate trade-offs, and reconsider decisions when necessary. We don’t stick to one implementation – we re-evaluate and adjust as needed (including throwing away the old one). Pragmatic choice of tools and avoidance of one-size-fits-all thinking. Bringing professionalism and empathy to how you work with others. Strong belief in solid technical fundamentals and preference for understanding and first principles over memorization.