• 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.

• Design, develop, and maintain robust, scalable, and secure data pipelines for ingesting, transforming, and validating clinical real-world data (RWD). • Implement software solutions using programming languages (e.g., Python, SQL) and cloud technologies (e.g., AWS). • Collaborate with software/quality engineers/analysts and product managers. • Communicate testing progress, risks, and quality metrics. • Analyze and fix defects identified within applications, services, or data pipelines. • Write SQL, Python, ELT code for querying clinical and genomic data. • Create data marts and perform data transformation and de-identification tasks. • Develop scalable data pipelines and processing workflows using AWS services. • Experience with Apache Airflow for workflow orchestration. • Apply version control (Git), CI/CD pipelines, and agile methodologies. • Support staff recruitment and onboarding, providing mentorship.

• Develop, maintain and evolve data ingestion and transformation pipelines following a medallion architecture; • Build and monitor integrations between the data lake and external applications (Octadesk, Salesforce and others); • Implement observability mechanisms for proactive failure detection, performance monitoring and cost control; • Collaborate with product, BI and engineering teams to understand needs and design scalable data solutions; • Document processes, pipelines and technical decisions to facilitate maintenance and onboarding of new team members.