Essential Knowledge & Experience

• Extensive hands-on experience (6+ years) in cloud-native data engineering, including 3+ years with Databricks and Delta Lake in Azure, combined with good (2+ years) in a technical leadership or architecture role.
• Deep practical experience of the Databricks ecosystem including Delta Lake, Unity Catalog, Declarative Pipelines, Spark Structured Streaming, Change Data Feed, Serverless SQL, and Cluster management & optimization.
• Expert-level SQL skills, with a track record of designing performant queries, optimizing data models, and driving insight generation from large datasets.
• Proficiency in multiple programming languages used in data engineering and analytics (e.g., PySpark, Scala, R, Python), with experience mentoring others in their use.
• Deep understanding of cloud-native data architecture, including Lakehouse design principles, data integration patterns, and automation strategies.
• Proven ability to design and deliver scalable, production-grade data pipelines, with a focus on reliability, maintainability, and performance.
• Strong experience with CI/CD, version control (Git), ideally including Databricks Asset Bundles, and Infrastructure as Code, leading DevOps practices in data engineering teams.
• Skilled in requirements gathering and solution design, translating business needs into technical specifications and guiding teams through delivery.
• Demonstrated leadership in troubleshooting and resolving complex data and code issues, driving root cause analysis and long-term fixes.
• Excellent communication and stakeholder engagement skills, with experience presenting architectural decisions, documenting processes, and influencing cross-functional teams.
• Commitment to continuous improvement, staying current with emerging technologies and fostering a culture of learning and innovation within the team.

Desirable Knowledge & Experience

• Hands-on experience with machine learning pipelines or integrating data engineering with advanced analytics and AI workflows.
• Experience designing and implementing data mesh or domain-oriented data architectures in large-scale environments.
• Test-driven development using common testing frameworks (e.g. pytest, nUnit, tSQLt, nBI, etc.)
• Exposure to real-time integrations or IoT solutions e.g. Event Hubs, Service Bus, IoT and Event Grid in Azure; IoT Core, PubSub in Google Cloud; etc.
• Familiarity with data governance frameworks, including data lineage, cataloging, and compliance (e.g., GDPR, HIPAA).
• Background in delivering data-driven solutions for commercial real estate applications
• Degree in IT, Engineering, computer sciences, business IT degree or in any quantitative discipline

Skills & Personal Qualities

• Strong communicator in English, both verbal and written, with the ability to engage effectively across global teams and all levels of the organisation.
• Analytical and curious mindset, driven to solve complex problems and understand intricate technical systems and architectures.
• Organised and detail-oriented, with a diligent, quality-focused approach and a strong sense of ownership.
• Collaborative and proactive, able to build relationships, resolve conflicts, and contribute meaningfully within cross-functional teams.
• Adaptable and dependable, capable of managing multiple priorities, meeting deadlines, and working independently or as part of a team.
• Continuous learner, demonstrating initiative in technical self-improvement and staying current with evolving tools and practices.

INCO: “Cushman & Wakefield”