Data engineer • leverkusen
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We are seeking a Lead Data Engineer for the Finance domain to join our Tech, Data & AI team. The successful candidate is a hands-on technical leader with deep data engineering expertise, strong communication skills, and a solid understanding of Finance data and processes in a complex environment such as reinsurance.
You will be responsible for leading data engineering activities for the Finance domain, ensuring robustness, scalability, and quality of data pipelines and analytical datasets supporting financial and regulatory processes. Beyond delivery, you will help shape how data engineering evolves at SCOR, including its role in enabling AI driven use cases.
This position is not a generic software engineering role: it requires strong ownership of data flows, data models, and analytics ready datasets that directly support financial reporting, IFRS 17 processes, performance steering, and management decision making.
Under the responsibility of the Head of Data Engineering, your mission will be to:
Lead data engineering activities within the Finance domain, supervising, coordinating, and planning your team’s work in line with Finance priorities and regulatory timelines.
Own end to end Finance data pipelines, from ingestion to consumption, ensuring reliability, scalability, auditability, and cost-efficient performance.
Provide hands on technical leadership by reviewing data pipelines and data services, enforcing state-of-the-art engineering practices.
Design and optimize large scale data processing solutions supporting Finance use cases, addressing challenges such as reconciliation, granularity, performance, and traceability.
Maintain architectural ownership of Finance data pipelines and datasets, enforcing clear documentation including code, lineage, data definitions, and release notes.
Ensure data quality, consistency, and governance across Finance datasets, in alignment with internal controls and regulatory expectations.
Coach and mentor data engineers, supporting skill development, autonomy, and a culture of engineering excellence.
Collaborate closely with Finance, actuarial, risk, data and AI stakeholders through workshops, design sessions, and agile ceremonies.
Actively contribute to the evolution of data engineering practices, particularly in the context of AI ready data platforms and finance analytics.
+7 years of experience as a Data Engineer with a strong data-centric mindset
+3 years of experience in a technical leadership role
Proven track record delivering and operating production-grade data pipelines in an agile environment
Proven experience with Palantir Foundry and / or Databricks
Experience in (Re)insurance, financial Services, or other complex data-intensive industries is a strong plus
Technical Skills:
Strong hands-on expertise in Python, PySpark and SQL.
Solid understanding of distributing data processing, CDC, slowly changing dimensions, and data modeling.
Strong exposure to CI / CD pipelines, Gitflows, and production best practices.
Good knowledge of REST API
Behavioral & Management Skills :
Software engineering first mindset with solid experience in data.
Curiosity and interest in learning the insurance/reinsurance business.
Strong analytical thinking, structure problem solving, and ownership attitude.
Excellent communication skills, with the ability to engage senior business and technical stakeholders.
Proven ability to lead, mentor, and inspire teams in a matrix, international environment.
