• Designing enterprise information architecture under limited direction to store, move, transform, deliver, integrate, and manage data. • Contributing to the growth of the Data Architecture practice by identifying common patterns and developing frameworks for all data management disciplines. • Executing the data strategy and vision in alignment with business goals by utilizing the modeling standards, guidelines, best practices, and approved modeling techniques. • Modeling the future state of the data architecture and provides best practices and guidelines. • Reviewing proposed data models in packaged or commercial applications for business applicability. • Contributing significantly to data integration, business intelligence (BI), and enterprise information management programs. • Establishing metrics to track and measure the value of data architecture initiatives (e.g., model reuse, project cost impact, data consistency improvements). • Identifying significant data assets based on business impact, decision impact, risk mitigation, or organizational relevance. • Ensuring critical information assets are represented in the enterprise information architecture (EIA) and comply with its design guidelines.

• 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.