• Collaborate with product managers, data scientists, analysts, and engineers to define business requirements and translate them into scalable data solutions. • Plan, design, build, test, and deploy data warehouse, data mart, and ETL/ELT pipelines—primarily within Google Cloud and BigQuery. • Lead small to medium data projects, including scoping, design, documentation, and execution. • Architect and operate cloud-based data pipelines that support reporting, invoicing, and analytics use cases. • Ensure data integrity, consistency, and accessibility across internal and external data products. • Develop, document, and enforce coding and data modeling standards to improve code quality, maintainability, and system performance. • Serve as an in-house data expert, making recommendations on data design, pipeline improvements, and best practices. • Build and maintain: Internal dashboards and reporting packages (weekly, monthly operational and financial reporting). • External reporting packages for customers, including standardized QBRs and trend analyses. • Monthly invoicing logic, automated data workflows, and standardized datasets for finance. • Work cross-functionally to expand usage and value of the data warehouse, promoting a data-driven culture across the organization.

• 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.