• Design, build, and maintain scalable data pipelines and APIs on Google Cloud Platform. • Develop automated workflows and data platforms that support analytics, reporting, and AI/ML use cases. • Implement best practices for data security, governance, CI/CD, and automated deployment. • Collaborate with data engineers, architects, data scientists, and business stakeholders. • Produce high-quality, reusable code and mentor team members on best practices. • Support testing, deployment, monitoring, and production troubleshooting.

• Define and evolve the vision, objectives and roadmap for the Data Platform, aligned with the company’s strategy • Manage and prioritize the backlog, balancing platform evolution, operations, governance and cost efficiency • Conduct continuous discovery, identifying opportunities and friction in the platform user experience • Translate business and technical team needs into initiatives focused on value and adoption • Act as a bridge between Engineering, Data, AI, Governance, FinOps and business areas • Manage trade-offs between speed, autonomy, control and efficiency • Monitor platform metrics (adoption, quality, lead time, efficiency and cost) • Promote topics such as self-service, governance, observability and reliability • Lead executive alignments and support strategic decisions

• Develop and implement data pipelines (batch and streaming) using Dataflow (Apache Beam) • Design and optimize BigQuery-based data warehouses and data marts • Work on data ingestion, transformation, and loading (ETL/ELT) using GCP services • Collaborate with business teams to understand requirements and convert them into scalable data solutions • Ensure data quality, performance, and cost optimization across GCP workloads • Support data modeling activities (dimensional and normalized models) • Assist in solution design and architecture discussions • Troubleshoot pipeline performance issues and optimize processing • Contribute to CI/CD and deployment automation for data pipelines • Work within agile teams and support sprint-based delivery

• Lead data engineering delivery to build reliable, scalable pipeline leveraging Snowflake, Sigma and other applications to support complex data analytics and self-service reporting. • Drive integration with popular tools and services in the broader data warehousing ecosystem. • Optimize architectural performance across the Data stack by refining execution parameters and enforcing best practice strategies to maximize resource efficiency and throughput. • Establish and evolve a data governance operating model across data quality, lineage, metadata management, stewardship, cataloging, and lifecycle management. • Perform advanced code-level analysis and resource profiling to identify and mitigate systemic root causes, ensuring the stability and reliability of high-scale production workloads. • Coordinate with internal stakeholders to ensure effective communication, issue resolution, and on-time delivery of projects. • Participate in weekend and weekday on call rotation.