Engineering Manager - Data Platform (m/f/d)
Babbel BerlinEst. Est. EUR 80,000–110,000 / yearSenior
Estimated range based on role, country and industry — not published by the company.
Key requirements
- Airflow
- Dbt
- Aws
- Terraform
- Ci/Cd
- Data Science
- Audit
Your Learning Journey in This Role
We are looking for an Engineering Manager to lead Babbel’s Data Platform team. The team owns the data infrastructure, pipelines and platform capabilities that support analytics, data science, product teams and engineering across the company.
This is a hands-on role. You will be responsible for both team health and delivery outcomes, creating an environment where engineers can do their best work while delivering reliable, scalable and well-governed data products. You will work closely with Data, Analytics, Product and Engineering leaders to turn strategy into sustainable execution.
How You’ll Make an Impact
Lead, coach and grow a data engineering team responsible for Babbel’s data platform and infrastructure.
Own reliable delivery and operational excellence across the platform, including quality, observability, SLAs/SLOs, on-call and incident management.
Partner with Analytics, Product, Data Science and Engineering teams to prioritise and deliver platform capabilities that make trustworthy data easy to discover, access and use.
Drive an architecture that supports governed AI and agent workloads at scale, with appropriate access controls, lineage, auditability and cost guardrails built in.
Establish clear ownership, strong engineering practices and a predictable delivery cadence while continuously improving platform performance, cost and sustainability.
Your Skills and Qualifications
We think you’ll need the following experience to succeed in the role:
Experience managing and developing data engineers, ideally in a data platform, infrastructure or internal-product context.
A strong track record of delivering and operating reliable production systems, including data pipelines, modelling, schema evolution, testing, CI/CD, monitoring and incident management.
Experience working with Product, Analytics, Data Science and Engineering stakeholders in cross-functional teams, balancing delivery, quality, cost and sustainability.
In addition, here are the skills and capabilities we’re looking for:
Technical leadership: Strong understanding of modern data platform architecture, including cloud infrastructure, data processing, streaming, orchestration and analytics tools. Experience with technologies such as AWS, Terraform, Databricks, Kinesis or similar, dbt and Airflow is relevant.
Data governance and responsible AI use: Practical understanding of access controls, IAM, least-privilege patterns, encryption, lineage, audit logging and governance for AI or agent workloads.
People leadership and team development: Creates a healthy, collaborative and accountable team culture through clear expectations, constructive feedback, coaching and career development.
Product and delivery leadership: Treats the data platform as a product for internal customers, communicates clearly, manages trade-offs and makes sound build-versus-buy, vendor, cost and reliability decisions.
Ways of working
You will work across Data, Analytics, Product and Engineering, building trusted relationships with internal customers and technical stakeholders. The role requires both technical depth and strong people leadership, together with the ability to create clarity, resolve ownership gaps and help teams deliver sustainably.
This role requires strong proficiency in English.
Please note that our company’s operating language is English, so you will need to be able to work in English.
Some perks of becoming a Babbelonian
Enjoy 30 vacation days and the chance to take a 3-month Sabbatical. Plus family and life situation counseling.
Decide how, when and from where you want to work with our flexible working hours and remote-friendly options as Jobbatical (up to 3 months inside the EU and UK) or work from our fully equipped office with nap, faith and family rooms.
Learn and grow with the internal learning opportunities, and use a yearly learning & development budget for external training. Learn languages w
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