Senior Manager, Data Engineering

Overview We are looking for an experienced Engineering Manager to lead the Data Engineering team at 7-Eleven. Enterprise Data powers decisions across the organization, with the Data Engineering team designing and operating the batch and streaming ELT pipelines, lakehouse models, and master data systems that feed enterprise reports and AI applications. You will lead a multi-pod team responsible for ingestion, transformation, modeling, and run-engineering across nine business domains. Responsibilities Manage day-to-day delivery across multiple Data Engineering pods (approximately 25–30 engineers across onshore/offshore teams), building and operating ingestion, ELT, streaming, and lakehouse pipelines across business domains such as Digital, Merchandising, Operations, Finance, Customer & Marketing, Fuel, and Supply Chain. Set the technical vision for Data Engineering—driving enterprise master data programs, historical/backfill strategy, intake maturity, change-control automation, and modernization of legacy ETL into a lakehouse architecture. Partner with Data Architects to translate conceptual and logical models into physical implementations using Delta/Lakehouse platforms—applying medallion patterns, dimensional modeling, and consistent intake contracts. Partner with Data Platform, Governance, and Quality teams to ensure every pipeline includes observability, data quality rules, lineage, and privacy classifications by design. Own delivery of legacy reporting and analytics modernization initiatives—refactoring OLAP-style models into dimensional models, repointing reports to modern BI platforms, and decommissioning legacy systems. Run the Data Engineering function as a product organization—managing backlog, business cases, enterprise prioritization, and adoption/value tracking for delivered data products. Mentor engineers, technical leads, and managers, and coach offshore engineering teams on modernization, code quality, and developer experience standards. Partner with Platform teams on CI/CD, AI-assisted SDLC, and cost attribution to ensure data workloads are observable, attributable, and cost-efficient. Own incident response and SLA adherence for production pipelines supporting critical business and AI-driven applications. Qualifications 8+ years of professional experience in Software Engineering, Data Engineering, or Analytics Engineering. 5+ years designing and operating large-scale data pipelines (batch and streaming ELT/ETL) in lakehouse or data warehouse environments. 5+ years working with distributed compute frameworks (Spark/Databricks strongly preferred; Delta Lake, Unity Catalog, Workflows, DLT a plus). 5+ years managing engineering teams, ideally across multiple pods with hybrid onshore/offshore delivery models. High proficiency with AI productivity and AI driven automation and agentic workflows. Strong knowledge of data modeling (dimensional/Kimball, data vault, medallion patterns) and experience translating conceptual models into physical implementations. Strong knowledge of master data management concepts and reference data governance. Strong knowledge of cloud data platforms (Azure preferred; AWS or GCP acceptable). Experience with data quality, observability, and lineage tooling, and embedding these into pipeline development. Experience operating production data pipelines, including SLAs, on-call, incident management, and Tier 2/3 support in collaboration with platform/SRE teams. Familiarity with AI/ML data pipelines, including feature engineering and ML-Ops handoffs. Strong communication and storytelling skills, with the ability to present technical concepts and progress to executive and business stakeholders. Strong project management discipline, including roadmaps, process documentation, and executive status reporting. #J-18808-Ljbffr