• Own the technical strategy and execution of migrating large-scale data workloads from GCP to AWS, ensuring continuity, data integrity, and minimal disruption. • Design migration playbooks and serve as the go-to expert for decisions across compute, storage, and orchestration layers during the transition. • Architect and implement scalable batch and streaming data pipelines using Apache Spark, Delta Lake, and the medallion architecture. • Establish standards for pipeline design, data quality, and observability that the broader engineering organization can build on. • Take accountability for the reliability, performance, and cost-efficiency of production ETL jobs running on AWS (EMR, Glue) against terabyte-scale datasets. • Proactively identify and address bottlenecks, technical debt, and opportunities to improve throughput and resilience.

• Design, build, and own scalable data platforms on Google Cloud • Play an architectural role, driving end-to-end data solutions • Define best practices and mentor team members • Work closely with stakeholders, analysts, and data scientists to deliver reliable, high-performance data pipelines and analytics platforms

• Build and maintain data infrastructure that enables the collection, storage, and retrieval of data; • Create new data flows by integrating our data sources and ensuring they are reliable and efficient; • Develop ETL pipelines, data warehousing, and data modeling to support business needs; • Ensure data quality monitoring, reliability, and lineage by developing processes and tools to identify and correct data quality issues; • Collaborate with other members of the Data & Analytics Team to optimize the data infrastructure and improve data governance; • Provide documentation and training to end-users on data sources, pipelines, and data quality procedures; • Stay current with the latest technologies and techniques related to data engineering, and identify opportunities to improve data infrastructure and analysis.

• Design, develop, and maintain scalable data pipelines for processing and analyzing large volumes of data • Develop and optimize data workflows using Databricks, leveraging Spark for large-scale data processing • Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure data integrity and quality • Utilize your expertise in Python for scripting and coding tasks related to data processing and analysis • Understand and implement business rules in Python for data transformation • Implement ETL processes to integrate data from various sources into data warehouse or data lake solutions • Optimize big data storage and processing • Troubleshoot and resolve data-related issues, ensuring the reliability and performance of our data infrastructure • Follow emerging trends and technologies in the data engineering space and make recommendations for continuous improvement • Optimize and tune data workflows for maximum efficiency and scalability. • Implement data security best practices to protect sensitive information and ensure compliance with data protection regulations. • Develop and maintain API integrations to facilitate seamless data exchange between systems and applications