• Own the design, development, and reliability of production data pipelines using Python, SQL, and modern orchestration tools • Architect and implement ETL/ELT workflows to move data between source systems, data warehouses, and downstream consumers at scale • Lead the development and optimization of data pipelines that feed machine learning models, including feature engineering, training data preparation, and inference pipelines • Design and maintain ingestion and chunking pipelines for RAG systems, including document parsing, embedding generation, and vector store population • Mentor and guide a more junior data engineer: conduct code reviews, pair on complex problems, and foster their technical growth • Organize and prioritize data engineering tasks, ensuring the team delivers reliably against business timelines and technical standards • Collaborate with data scientists, ML engineers, and product teams to productionize models and ensure reliable, performant data delivery • Design and implement monitoring, alerting, and data quality frameworks to proactively catch pipeline failures and data drift • Drive the design and evolution of our data warehouse and data lake architecture, making strategic decisions about tooling, partitioning, and performance • Work with stakeholders across the business to understand data needs and translate them into scalable, well-documented data solutions • Champion engineering best practices: documentation, testing, CI/CD, and knowledge sharing within the team

• Architect, implement, and optimize cloud-based solutions while leveraging industry best practices. • Provide mentorship and guidance to junior engineers and drive continuous improvement and innovation across our CloudOps and DevOps practices. • Collaborate closely with cross-functional teams to design and deploy robust, automated systems that support our organization's mission-critical applications and services. • Monitor and maintain the health of our cloud environment, identifying and resolving issues proactively to minimize downtime and incidents.

• Strong experience with both Adobe Analytics • Experience in AA strategy and implementation expertise • Implementation experience with Adobe Launch • Understanding of web analytics tool basics: tags, cookies, variables. • Strong understanding of tag management systems: tags, rules, and variables • Experience in Vue.JS, JavaScript, jQuery, CSS and HTML skills, and able to be a leader in this area of expertise • Strong attention to detail and QA abilities • A solid understanding of advertising, marketing and strategic brand management and how to best leverage these in a digital environment • Experience presenting in front of groups, to clients, and via web conference • History of working with new business teams on requests for proposal/information and presenting analytics documentation demonstrating agency skillsets. • Experience in creating case studies, point of view documents or white papers in line with your job function in analytics. • Work with internal team to continually streamline processes and find efficiencies in the day-to-day work processes done by the data platforms team. • Experience working on testing, targeting and personalization projects with both targeting and optimization tools and data management platforms (DMP).

• Own the architecture and operations of our data lakehouse, including object storage, table formats, maintenance, and query engine integrations • Build and maintain the infrastructure layer that transforms and serves data reliably at scale—from raw landing zones through to curated, queryable datasets • Partner with product engineering to establish data contracts and schema standards around event telemetry, ensuring data arrives in the lakehouse in a form that's reliable and ready for downstream use • Drive decisions on data platform architecture, tooling, and engineering best practices across storage, compute, and access layers • Enhance observability and monitoring of data infrastructure, including pipeline reliability, data freshness, and system performance • Partner cross-functionally with teams across Analytics, Infrastructure, and Product to understand data needs and deliver impactful platform solutions • Provide product feedback by dogfooding new data infrastructure and AI technology