Overview

Role Description & Responsibilities

Responsibilities

• Consult with OEs in the implementation and configuration of core platform components across Databricks from initial deployment through to stable production.
• Assist in building data integration pipelines that connect OE source systems to central platforms, using both batch and streaming patterns.
• Configure platform capabilities with high availability, strong access controls, and end-to-end observability.
• Extend the platform offering into AI and GenAI scenarios, including Azure AI Foundry, so that model-based use cases run on governed foundations.
• Partner directly with Operating Entities to translate business needs into working platform implementations and reusable solutions.
• Implement governance, security, and data control layers in line with financial services expectations for data handling, auditability, and operational resilience.
• Build and refine CI/CD and infrastructure-as-code pipelines to keep deployment cycles fast, secure, and repeatable.
• Collaborate closely with security, risk, compliance, architecture, and data governance teams to clear the path for platform adoption.
• Deliver reusable components, reference architectures, and engineering best practices that accelerate platform rollout across the group.
• Support the rollout of central platform solutions to the OEs, and build business-specific use cases on top of them.
• Coach and mentor engineers across the organisation, and drive continuous improvement in tooling, standards, and ways of working.

Qualifications

• Bachelor/Master degree in Computer Science, Information Systems, Engineering, or an equivalent technical discipline.
• 5+ years of hands-on experience implementing or engineering data and analytics platforms in enterprise environments, ideally within financial services, insurance, or another regulated industry.
• Significant experience with Databricks, including Delta Lake, Unity Catalogue, workflows, and production pipelines.
• Strong data integration skills, covering ETL and ELT design, orchestration (e.g., Azure Data Factory, Airflow), and both batch and streaming sources.
• Solid engineering skills in Python and SQL, with experience building and maintaining production-grade code.
• Experience with CI/CD pipelines, source control management, and infrastructure-as-code (e.g., Terraform, Bicep).
• Working knowledge of platform security and governance concepts, including access controls, encryption, and data lineage.
• Familiarity with end-to-end (E2E) Data Product Management, including data governance, quality, and lifecycle management.
• Excellent communication and stakeholder management skills in English (additional languages are a plus).

Nice to have

• 7+ years of experience in platform engineering, data engineering, or cloud infrastructure.
• Exposure to Azure AI Foundry or comparable AI platform tooling, including the deployment of model-based services.
• Familiarity with MLOps practices and tools such as MLflow, feature stores, and model lifecycle governance.
• Experience with Kubernetes, Docker, and cloud-native architectures.
• Hands-on familiarity with the wider modern data stack (e.g., Spark, dbt, Kafka, Snowflake, or similar).
• Practical experience with Azure data services such as Azure Synapse Analytics and Microsoft Fabric.
• Experience with observability and monitoring tools such as Azure Monitor, Datadog, or Prometheus.
• Relevant certifications such as Databricks Certified Data Engineer, Microsoft Azure Data Engineer, or equivalent.