• Design, develop, and maintain core Python ETL framework • Develop and optimize automated refresh pipeline with AWS services • Build Python integrations with external systems • Identify and eliminate manual bottlenecks in data onboarding • Ensure data integrity and security throughout project lifecycles

• Complete analysis, design, and development of BI solutions • Database development primarily in SSIS, Databricks, and SQL • Collaborate with other developers to create and implement the best approach solution(s) • Review queries and troubleshoot for performance issues • Assist with the analysis and extraction of relevant information from large amounts of historical business data to feed data science initiatives • Participate and support the design and documentation of processes for large-scale data analyses, model development, model validation, and model implementation • Support and maintain a positive safety culture by following all safety policies and procedures and actively contributing to a safe working environment

• Design the enterprise architecture for the Databricks Lakehouse, including medallion architecture (Bronze, Silver, Gold) and semantic layers. • Develop multi-cloud data ingestion and consolidation strategies across AWS, Azure, GCP, and on-prem systems. • Define standards for data lineage, metadata management, data quality, CDC/SCD, streaming, and batch ingestion. • Establish enterprise data governance frameworks, including classification, cataloging (Unity Catalog), retention, and security policies. • Ensure compliance with global regulatory standards such as GDPR, PIPEDA, Australian Privacy Act, and SOC 2. • Architect scalable, reliable, and cost-efficient solutions to support analytics, reporting, GIS, and AI/ML workloads. • Create architecture documentation, standards, and best practices. • Collaborate with data engineering teams to implement pipelines, Delta Lake storage, and Databricks workflows. • Provide technical leadership, architectural guidance, and code reviews. • Ensure consistent architecture implementation across products, tenants, and cloud environments. • Work with DevOps teams on infrastructure, networking, identity management, and CI/CD integration. • Establish monitoring, observability, and operational governance processes. • Define and enforce security policies including RBAC/ABAC, encryption, and audit logging. • Integrate enterprise metadata and lineage using Unity Catalog and Purview. • Ensure compliance with data residency, regulatory, and cloud-specific requirements. • Develop standards for data retention, archival, and disaster recovery. • Design architecture supporting BI tools such as Power BI and Databricks SQL. • Enable advanced analytics, predictive modeling, geospatial analytics, and AI/ML use cases. • Establish semantic modeling standards to ensure consistent business definitions across products. • Serve as a subject matter expert in Databricks Lakehouse and multi-cloud data architecture. • Mentor engineering teams on data modeling, governance, and scalable architecture practices. • Collaborate with product teams and stakeholders to translate business requirements into technical solutions. • Participate in architectural governance, vendor evaluations, and roadmap planning.

• Responsible for designing, building, and maintaining efficient, scalable, and fully automated data pipelines and architectures that support various business needs • Design, develop, and maintain scalable data pipelines to collect, process, and transform data from various sources • Integrate data from multiple sources, ensuring data quality and consistency across the organization • Build and maintain data storage solutions, including data warehouses and data lakes, ensuring optimal performance and reliability • Implement data transformation and enrichment processes to prepare data for analytics and reporting • Leverage cloud technologies, particularly AWS, to optimize and manage data infrastructure • Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver high-quality data solutions • Create and maintain comprehensive documentation for data pipelines, data models, and related processes • Mentor and guide junior data engineers/analysts on data engineering best practices and industry standards