Senior Data Engineer

Senior Data Engineer Contract - initial 3 months Onsite in London 5 days per week at Customer The ideal candidate blends strong commercial acumen and consulting chops with hands-on technical execution—specifically leveraging the Databricks Data Intelligence Platform to solve complex enterprise data challenges. Key Responsibilities • Technical Leadership & Consulting: Partner with key client stakeholders (CDOs, Lead Architects, Enterprise Data Teams) to translate complex business requirements into scalable, modern data solutions. • Databricks Solution Architecture: Design, build, and optimize enterprise data pipelines, lakehouses, and analytics platforms utilizing the Databricks unified analytics stack. • Project Delivery: Take hands-on ownership of technical deliverables across the entire software development lifecycle (SDLC)—from discovery, proof-of-concept (PoC), and design to deployment, performance tuning, and hypercare. • Best Practices & Governance: Champion enterprise data governance (Unity Catalog), DataOps, CI/CD pipelines, and cost-optimization strategies within client environments. • Stakeholder Management: Deliver engaging technical presentations, workshops, and solution demos to both technical and non-technical audiences. Role Requirements & Qualifications Core Requirements (Non-Negotiable) • 4+ Years of Data Engineering Experience: Proven track record building enterprise ETL/ELT pipelines, data lakes, and data warehouses. • 4+ Years of Consulting / Client-Facing Experience: Demonstrated success in technical consulting, solution design, client management, and delivering engagements within agency, SI, or professional services environments. • Certifications: Must hold the Databricks Certified Data Engineer Professional credential (or actively maintained equivalent). • Proven Databricks Track Record: Minimum of 2 to 3 enterprise-scale projects delivered with hands-on, end-to-end implementation experience on Databricks. Technical Skillset • Databricks Stack: Deep experience with Delta Lake, PySpark/Spark SQL, Delta Live Tables (DLT), Unity Catalog, Databricks Workflows, and Auto Loader. • Languages: Advanced proficiency in Python and SQL. • Cloud Infrastructure: Strong hands-on experience on at least one major cloud provider platform (AWS, Azure, or GCP). • Data Modeling & Architecture: Expertise in Medallion Architecture (Bronze/Silver/Gold), dimensional modeling, and real-time/batch processing. • DevOps & Software Engineering: Experience with Git, CI/CD tools (e.g., Azure DevOps, GitHub Actions), and infrastructure-as-code principles.