• 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

• Build data pipeline, ELT and infrastructure solutions to power internal data analytics/science and external, customer-facing data products. • Create automated monitoring, auditing and alerting processes that ensure data quality and consistency. • Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models • Design, develop, implement, and optimize existing ETL processes that merge data from disparate sources for consumption by data analysts, business owners, and customers • Seek ways to continually improve the operations, monitoring and performance of the data warehouse • Influence and communicate with all levels of stakeholders including analysts, developers, business users, and executives. • Redesign and optimize existing data processes using AI (e.g., LLM-assisted development, automated documentation, issue resolution) to improve team productivity and enhance data quality across the data warehouse • Live by and champion our values: #day-one, #ownership, #empathy, #humility.

• Contribute to and execute a roadmap that positions TheKey's data platform as AI-ready — well-structured, richly documented, and accessible for machine learning and generative AI use cases. • Champion the adoption of AI and automation tools within the data engineering team to increase delivery velocity and reduce manual effort. • Partner with business and technology stakeholders to align platform capabilities with analytics and AI initiatives. • Lead the design and governance of a metadata-rich data lake in Google BigQuery, ensuring datasets are tagged, documented, and contextualized. • Establish and enforce standards for data cataloging, semantic tagging, lineage tracking, and business glossary definitions. • Drive adoption of tools such as Google Dataplex or equivalent for automated metadata management and data quality enforcement. • Build and maintain scalable data pipelines, leveraging AI-assisted development tools to accelerate development and reduce errors. • Implement AI-driven testing and observability frameworks to automatically validate pipeline outputs, detect anomalies, and enforce data quality. • Define and enforce data governance frameworks that support both regulatory compliance and AI readiness. • Own master data management practices to ensure accuracy, consistency, and a single source of truth for critical business entities. • Directly manage a team of data engineers – providing mentorship, clear expectations, and career development support.

• Lead the design, development, and optimization of scalable data solutions. • Define data architecture strategies and ensure best practices are followed. • Oversee and guide the creation and automation of data pipelines and platforms. • Establish and enforce data quality and governance frameworks. • Collaborate with Architects, Product Owner, Data Scientists, and DevOps to align data solutions with business needs. • Research and evaluate emerging data technologies and methodologies. • Ensure seamless integration of data management solutions into client environments. • Develop risk mitigation strategies and implement data recovery plans. • Lead the development of data repositories, including data warehouses, data lakes, and operational data stores. • Mentor and support the development of junior and senior team members.