Data Engineering Lead (m/w/d) (Berlin)

We are looking for a

Data Engineering Lead (m/w/d)

(unlimited, full-time) Join our team at our location in Berlin, Verl, Baden-Baden or Oslo – hybrid working conditions available.

(Y)our Mission:

The Data Engineering Lead leads the design, development, and delivery of high-quality data pipelines and data products that power analytics, BI, and AI across our fintech ecosystem in payments, dunning, invoicing, and collections. This leader will build and scale a high-performing data engineering team focused on transforming raw data into trusted, accessible, and reusable assets — ensuring that the broader organization can make faster and smarter decisions.

Working in an agile, cross-functional data product model, this role is accountable for the results and contributions of the data engineering discipline — ensuring that the data engineers deliver trusted, timely, and high-quality data to enable business and analytical outcomes.

Your key responsibilities:

Strategic Leadership

• Define and execute the data engineering vision and roadmap aligned with the overall Data, AI & Analytics strategy.
• Establish and continuously improve the operating model for data engineers within agile data product teams, ensuring clear accountability for delivery outcomes (timeliness, quality, completeness, compliance).
• Champion the adoption of modern data engineering and agile delivery practices, fostering close collaboration with product owners, BI, data analysis, data science, data platform, and tech teams.

Data Pipelines & Modeling

• Oversee the development of robust ETL/ELT pipelines to ingest and transform data from multiple internal and external sources.
• Ensure that agile data product teams deliver fit-for-purpose data models that meet the needs of analytics, AI, and regulatory reporting.
• Drive excellence in data modeling and pipeline design, ensuring solutions are efficient, maintainable, and well-documented.

Data Quality & Reliability

• Implement data quality frameworks and automation across pipelines owned by agile teams.
• Define and monitor data SLAs and SLOs, ensuring that product teams deliver data that meets business needs in terms of timeliness, accuracy, and availability.
• Promote proactive data reliability engineering, enabling teams to detect and resolve issues early.

Collaboration & Stakeholder Management

• Collaborate closely with Data Product Owners to prioritize and deliver data engineering work in alignment with business priorities.
• Partner with Platform Engineering teams to ensure smooth operation of data pipelines within the shared core data platform.
• Collaborate with the Business IT teams to create reliable and robust interfaces to the source systems
• Work hand-in-hand with Data Governance and Data Architecture to ensure alignment on metadata, lineage, and data ownership.

Team Leadership & Development

• Lead, mentor, and grow a high-performing team of data engineers working across multiple agile data product teams.
• Ensure consistent technical standards, delivery practices, and performance management across the discipline, even within decentralized team setups.
• Cultivate a culture of ownership, accountability, and collaboration within and across agile data product teams.

Process & Operational Excellence

• Promote automation, CI/CD for data, and observability across all data engineering workstreams, including AI-based productivity increases.
• Establish KPIs for engineering productivity, pipeline performance, and data delivery quality within product teams.
• Contribute to the evolution of our data-as-a-product approach, ensuring data products are discoverable, well-documented, and reusable.

What you bring:

• 10+ years of experience in data engineering, with at least 3–5 years in a leadership role managing multi-team delivery, with overall team size >10
• Proven success in leading data engineering functions within agile, cross-functional data product teams.
• Strong technical expertise in Azure, SQL, Python, and modern data transformation and orchestration frameworks (e.g., dbt, Airflow, Spark)
• Deep experience with cloud-based data lakehouses (Azure cloud, Databricks Medallion architecture)
• Experience in fintech or financial services is a strong advantage.
• Expertise in data modeling, transformation, and quality assurance for analytical and operational use cases.
• Strong knowledge of data architecture principles and data product thinking.
• Excellent communication and stakeholder management skills — especially in cross-functional agile environments.
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