Data Platform Team Lead

Requirements

• 5+ years in data engineering or data platform roles, including 2+ years leading projects or people
• Deep SQL and strong Python. Production experience with Airflow, dbt or comparable frameworks
• Hands‑on expertise with cloud warehouses (BigQuery, Snowflake) and at least one major cloud provider (AWS or GCP)
• Proven CI/CD and IaC chops (GitHub Actions, Terraform or similar)
• Solid grasp of data modelling, governance and observability patterns
• Experience integrating with or building against business systems such as ERP, Shop or even Bank integrations. You are comfortable owning that layer end to end
• A track record of working closely with Commercial, Finance, Operations or Analytics stakeholders and treating them as first‑class partners
• A bias to action in ambiguous situations. You would rather make a reasonable call and adjust than wait for perfect information
• Hands‑on fluency with agentic coding AI tools (like Claude Code) and a track record of using AI to accelerate engineering work
• Excellent English communication. German is a plus
• Bonus: exposure to AI/LLM workloads or vector databases on the production side
• Operating in a fast‑moving, ambiguous environment where the plane gets built while you are flying in it. You like being pragmatic and bringing in structure without slowing the business down
• Coordinating across many stakeholders. You move easily between an engineering deep‑dive in the morning and a Commercial leadership conversation in the afternoon
• Staying hands‑on as a leader. You are happy to write SQL, debug an integration or scope an internal tool yourself when that is what the situation calls for
• Translating between worlds. You take messy business problems and turn them into clear technical scope. You take technical trade‑offs and turn them into concise narratives executives can act on
• Coaching and unblocking engineers and analysts through clear expectations and constructive feedback
• Seeing the platform‑level picture: designing for security, self‑service and future scale rather than one‑off pipelines
• Even if you don’t meet every single point in the job description, we still encourage you to apply. We’d love to hear from you!

What the job involves

• Join a high‑trust, cross‑functional team where data sits at the heart of every product decision, commercial bet and operational process
• You will work side‑by‑side with our Analytics team, Product, Engineering, Commercial, Medical and Operations colleagues to turn a modern, cloud‑native stack (GCP/AWS, BigQuery, Airflow, Airbyte, dbt, Looker, Mixpanel, Claude Code) plus a growing set of integrations and internal tools into reliable insights that move the business forward
• Your work will guide decisions that reach medical students and clinicians worldwide, directly advancing our mission to enable all medical professionals to provide the best possible care
• As Data Platform Lead you will be the people manager of a small, senior team and the domain expert steering AMBOSS's end‑to‑end data platform strategy
• That said, the role is broader than a classic data platform mandate
• Beyond owning platform evolution and pipelines, you and your team also own the integration layer to our ERP and other core business systems
• On top of that you build small services and internal tools that solve specific business and operations problems
• One current example is our master data management service for medical institutions around the world, which powers B2B commercial work and product experience
• This role is more dynamic and more business‑forward than a pure engineering leadership position
• Communication with stakeholders across Commercial, Product, Finance, Operations and Medical is constant
• Engineering excellence still matters, but coordination, prioritization, fast judgement calls and a strong hands‑on mentality matter at least as much
• Set the course. Own and sequence the roadmap across the data platform, ERP and business systems integrations, plus the internal tools and services your team builds and runs
• Own the Single Source of Truth for the entire company so Analysts, Scientists and stakeholders across departments speak the same data language and use the same metrics
• Develop the team. Hire, mentor and grow 3 to 5 Data Engineers. Run clear goal‑setting and feedback cycles
• Architect for scale. Design and review core components: batch and real‑time pipelines, modeling layers, integrations, governance, observability and CI/CD so Analysts, Data Scientists and other Engineering teams can self‑serve with confidence
• Build internal services and tools to unblock the business. Our master data management service for medical institutions is one example. Expect more of these as the business grows
• Partner with Commercial, Finance, Product, Operations and Medical. Translate ambiguous business needs into systems and data products that move the company forward
• Navigate ambiguity and stay hands‑on. Create structure where there is none, sequence the work pragmatically and make the call when there is no obvious right answer. This is not a step‑back‑and‑review‑PRs role. You will be in the work alongside your team on the highest‑leverage problems
• Raise the bar. Codify engineering standards for code quality, testing, reliability and incident response. Scale agentic coding and AI‑assisted workflows so a small team can punch above its weight
• Stay tool‑agnostic: evaluate, pilot and when needed retire technologies based on evidence rather than hype