• Build and maintain ETL pipelines and code in cloud data platforms. • Support ingestion activity and onboarding of new data sources. • Work with the Databricks Data Intelligence Platform and develop Data Engineering workloads. • Construct raw, refined and curated data layers; catalogue assets appropriately. • Validate solutions against functional and non-functional requirements. • Deliver datasets , transformations and performance-optimised data products. • Improve processes, engineering patterns, and reusable tooling. • Monitor and measure pipeline performance; support incident resolution. • Ensure documentation meets acceptance standards and is approved centrally. • Actively engage in Agile ceremonies and governance forums.

• Design, develop, and maintain scalable data pipelines and ETL processes using Azure Data Factory, Azure Databricks, and other Azure services. • Own the data engineering framework, including pipeline patterns, orchestration standards, and reusable components. • Collaborate with data scientists, Software engineers, analysts, and other stakeholders to understand data requirements and deliver high-quality data solutions. • Define, document, and enforce best practices for ADF, Databricks, Spark, and data modeling. • Implement and maintain data storage solutions using Azure SQL Database, Azure Data Lake Storage, and Azure Cosmos DB. • Ensure data quality and integrity by implementing data validation, cleansing, and transformation processes. • Implement data quality checks, validation frameworks, and monitoring for critical data assets. • Design and support governance patterns leveraging Databricks Unity Catalog and Azure-native controls. • Develop and maintain documentation for data engineering processes and solutions.

• Architect and deliver complex, multi-year data unification and Customer 360 solutions. • Design and implement Master Data Management (MDM) capabilities: identity resolution, match/merge logic, survivorship rules, and stewardship workflows. • Build and operate scalable, automated data pipelines across cloud and on-prem sources; hands-on experience with Profisee (MDM) and Fivetran (integration). • Develop and optimize solutions on Azure Data Lake, Azure Synapse, Azure Data Factory, Power BI, and Microsoft Fabric (including lakehouse/medallion patterns). • Establish and operationalize data governance frameworks: data cataloging, lineage tracking, stewardship operating models, access controls, and regulatory/compliance policies; experience integrating Microsoft Purview is a plus. • Define and monitor data quality baselines (accuracy, consistency, completeness, timeliness) and drive continuous improvement. • Create and evolve semantic models to support analytics, reporting, and AI scenarios; enforce standardized definitions and calculations. • Lead change management and stakeholder engagement to drive adoption and value realization. • Apply DataOps practices for observability, performance tuning, hypercare, and post–go-live optimization. • Collaborate with executive, business, and IT stakeholders; translate business needs into clear technical designs and roadmaps.

• Develop, enhance, and troubleshoot Mainframe batch processes using JCL, Easytrieve, and SAS. • Build and maintain automation and data processing scripts using Python. • Support distributed data processing workloads using Apache Spark and the PySpark API. • Write efficient SQL queries for data extraction, analysis, and transformation. • Work with Google Cloud Platform (GCP) services - primarily Cloud Storage - for data movement and storage management. • Collaborate with data analysts, engineers, and business teams to support data initiatives and enhance data workflows. • Participate in documentation, code reviews, and best practices for data and code quality. • Investigate data issues, perform root-cause analysis, and implement corrective actions.