Data Engineering Manager

A unique professional challenge to taking part in building up a completely new R&D strategic hub from the ground up for a stable, market-leading enterprise. Manager-level benefits including an annual target bonus. Additional perks: a cafeteria allowance, private health insurance package Flexibility: Hybrid working model requiring 3 days of office presence and offering 2 days of home office.

Organisation/Department We are seeking an experienced Manager, Data Engineering to lead a team building and scaling our Client's data infrastructure. You will combine hands-on technical expertise with strong leadership skills, managing both local and distributed teams while maintaining direct involvement in architecture decisions and technical implementation.

Job description Lead and mentor a team of data engineers, fostering technical growth and accountability Design and oversee data pipeline architecture from ingestion through delivery, ensuring scalability and reliability Own data quality, cleaning, ETL processes, and delivery mechanisms serving analytics and ML/AI operations Collaborate with data science and MLOps teams to support model development and production deployment Stay current with data engineering best practices and emerging technologies; drive technical decision-making

Requirements 4+ years in a team lead, principal engineer, or management role leading engineers (local and/or remote teams) 5+ years as a hands-on data engineer with recent, demonstrated experience in modern tech stacks Direct experience in change management, scaling teams, remote / hybrid / onsite team coordination Significant production experience with the Databricks Lakehouse on AWS — Unity Catalog, Delta Live Tables, Databricks SQL, and Databricks Workflows. Working knowledge of supporting AWS services (S3, IAM). Deep expertise in data pipeline design, ingestion, transformation, quality assurance, and delivery Professional experience in data-driven organizations with insights and ML/AI as core business functions Hands-on FinOps — owning compute spend, building cost visibility, and driving costoptimization initiatives Working knowledge of data science, ML/AI operations, and model deployment pipelines Preferred Qualifications: Production AI/ML systems experience where data engineering directly enabled product features Experience managing teams across multiple time zones or geographic locations Familiarity with data governance, compliance, and metadata management at scale Track record of building and scaling data platforms that support analytics and operational intelligence What We Value Technical depth combined with leadership ability—you build systems and develop people Proven problem-solving and architecture design skills in complex, data-heavy environments Pragmatism over perfection; ability to balance technical excellence with business timelines Collaboration across teams and comfort partnering with analytics, data science, and engineering organizations Stakeholder fluency with SRE, DBA (SI: work with them also), BI/Analytics, Data Science and Product teams AI-leveraged delivery comfortable with the team using Claude Code, Cursor, or Databricks Genie to accelerate pipeline development