Lead Data Platform Engineer

line management responsibility for a small squad. You will remain close to the code, set technical direction for the platform, partner closely with the rest of the team to productionize what we build while partnering with other technical and business stakeholders. What You Will Do - Own the Data Science Platform Roadmap:Define and drive the technical strategy for the DSP on Azure Databricks, balancing platform stability, self-service capabilities, and the evolving needs of data scientists and AI engineers. - Productize Analytics Solutions:Lead the productization of the advanced dashboarding and analytics data products, turning prototypes into reliable, well-governed, scalable data products that can be reliably consumed by end users. - Build Self-Service Foundations:Design and operate the lakehouse architecture, data pipelines and curated models, and governance patterns (Unity Catalog, dbt, access controls) that enable analysts and scientists to work autonomously on financial data. -

Lead and Grow the Squad:Line manage and and mentor a small team of data platform engineers, set engineering standards, review design and code, and coordinate with rotating contributors to deliver against the roadmap. - Partner Across Teams:Collaborate closely with the MLOps / Platform Engineering team on CI/CD and observability, with the AI Solutions and Productization squads on data needs, and with business stakeholders to align the platform with real analytics and self-service use cases. What You Bring - Technical Expertise:Strong hands-on experience with Azure Databricks, Spark, Delta Lake, dbt, SQL, and Python. Solid command of lakehouse architectures, data modeling, analytics engineering practices and production-grade data pipelines. - Platform Mindset:Proven experience designing and operating data platforms in enterprise or regulated environments, including Unity Catalog governance and cost/performance optimization. - Player-coach Leadership:Experience leading or mentoring a small technical team while staying hands-on in architecture, implementation, code reviews, and production delivery. - Productization & Self-Service:Experience taking analytics or data products from prototype to production and enabling self-service consumption by non-platform teams (data scientists, analysts, business users). - Domain Knowledge:Familiarity with financial data structures and the demands of data-driven systems in complex, regulated organizations is highly desirable. - Experience & Education: 5+ years in data engineering, analytics engineering, or data platform roles. Relevant degree in Computer Science, Engineering, or related field; Azure or Databricks certifications are a strong plus. English fluency is required. If you have any questions, check out our FAQ page or call Yuliya Stoyko at . For this vacancy we only accept direct applications. Diversity is important to us. Therefore, we are looking to receive applications regardless of any personal background. What We Offer Flexible Work Models We trust our employees and offer a work environment that is well-balanced, productive and fosters success. Personal Development You will benefit from a culture of continuous learning and feedback. Your personal growth is supported through an extensive learning offering. Agile Working Methods Whether through scrum or design thinking, we solve exciting tasks together in teams. J-18808-Ljbffr