Lead Data Engineer

The Opportunity The Marketing Operations and Engineering team in Growth Marketing and Insights (GMI) handles data architecture, transformation, and engineering for GMI under the Marketing org. We manage backend data for marketing measurement tools and processes such as attribution, product and customer segmentation, and lifetime value. We're looking for a Lead Data Engineer to build and own the data foundation behind Adobe's marketing measurement platform. You will collaborate with business analysts and data scientists to define requirements for new and existing models. You will build data pipelines, improve ETL processes, maintain data quality, and seek ways to boost efficiency and automation in daily tasks. You will oversee the data platform behind Adobe’s marketing models and other marketing measurement and reporting tools used company-wide. The initial focus of this role is Adobe’s B2B marketing and sales data, though the scope is encouraged to grow beyond B2B as the team’s remit expands. What you'll Do We are seeking a proven contributor who will provide support in data engineering, building data assets, and software applications. This individual should be able to provide technical direction and system architecture advice for individual initiatives. Collaborate with key partners and apply your technical proficiency across different stages of the software development p [... source excerpt omitted ...] el design artifacts. Deliver high-quality code for a module, lead validation across all types of testing, and support activities related to implementation and transition. Assume responsibility for restructuring the database to better support data scientists and business analysts across the marketing organization. Scope requirements, connect data sources, and build ETL processes to support new products and routes to market as the team expands campaign measurement products and models for Adobe’s B2C and B2B business units. Collaborate with Marketing, Finance, and Sales groups to ensure data accuracy and conduct root cause analysis when inconsistencies occur in the data. Incorporate [... source excerpt omitted ...] lot into the development process. These tools help scaffold pipelines, generate tests, and speed up code reviews. You will still be responsible for the quality and accuracy of all production code. Find opportunities to automate recurring engineering work — pipeline monitoring, failure triage, documentation — using scripted workflows or AI agents where they genuinely save the team time. What you need to succeed BS/MS in Computer Science or equivalent experience, with 8+ years of data engineering experience. Proven expertise in SQL, Python, Spark, and data pipeline orchestration tools (e.g., Airflow, dbt). Hands-on experience with cloud platforms (AWS, Azure, GCP) and cloud-base