Senior Manager, Enterprise Data Platform

## Senior Manager, Enterprise Data Platform Apply locations:
SSC Irving TXtime type:
Full time posted on:
Posted Todayjob requisition :
R26 3431
* 7-Eleven is an iconic family of brands with over 86,000 locations, surpassing every retailer in the world. We revolutionize convenience, restaurants and fuel through cutting edge innovation — working hard to be the customer's first choice. 7-Eleven empowers our employees to "activate awesome" and make a meaningful impact in their stores and communities every day. If you're ready to grow, lead and make a difference, come join our team and help shape the future of convenience.
* 7-Eleven is looking for an experienced Engineering Manager to lead the Enterprise Data Platform team.

At 7-Eleven, Enterprise Data is the aggregator of all large-scale datasets across the organization. We operate the Enterprise Data Lakehouse and Analytics Platform on Azure + Databricks (Unity Catalog), producing reports, metrics, and AI/ML-driven insights for users ranging from C-level executives to store managers. The Enterprise Data Platform team owns the infrastructure, governance plumbing, and automation that lets our Data Engineers, Analysts, and Data Scientists move at full speed — and ensures every dollar of our  annual platform spend is observable, attributable, and tied back to a data-product owner.###

Responsibilities
* As the Senior Manager of the Data Platform team, you will:
* Manage day-to-day delivery across multiple Platform Engineering pods building and maintaining the cloud infrastructure and automated processes that power Data Engineering, Analytics, and Machine Learning verticals.
* Set the multi-year vision for the Data Platform — identifying gaps in the stack, defining a roadmap (Workspace migration, Serverless adoption, CI/CD, AI-assisted SDLC, cost attribution), and positioning EDP to deliver data-product-driven applications at enterprise scale.
* Partner with Data Architects to design scalable, maintainable, cost-efficient pipelines and shared services — meeting best practices for cloud infrastructure, lakehouse modeling, and analytics engineering.
* Partner with Data Governance, Privacy, and Compliance leaders to scale the rollout of governed data products — including auditing, lineage, discoverability (Alation), data quality, and privacy controls (e.g., consumer rights handling).
* Drive cost attribution and chargeback — extend tagging coverage from workspace-level to table/compute/job-level, accelerate Databricks Serverless adoption, and give product owners visibility into their true unit economics.
* Lead change management for new data initiatives and tools — running presentations, workshops, office hours, and training material to drive adoption across 7-Eleven.
* Mentor engineers, technical leads, and engineering managers in their personal growth and careers — including cross-coaching with our GSC counterparts.
* Own the Site Reliability and Support function for the Enterprise Data Platform — incident management, automated observability, ticketing, ITSM, and 24×7 on-call.### Qualifications
* All qualifications are representative of the experience level we are targeting — you do not need to meet every bullet.
* 8+ years professional experience as a Software Engineer, Data Engineer, Dev Ops Engineer, or similar.
* 5+ years in Dev Ops, Platform Engineering, or Cloud Infrastructure.
* 3+ years in Data Engineering, Data Analytics, or AI/ML.
* 3+ years managing engineering teams — preferably multiple teams concurrently, including hybrid onshore/offshore (SSC + GSC) models.
* Experience owning Incident Response, on-call rotations, knowledge bases, and ITSM tooling.
* Deep technical knowledge of cloud infrastructure — Azure preferred; AWS or GCP acceptable.
* Deep knowledge of application and infrastructure observability — logging, monitoring, alerting, and automated incident tracking.
* Strong knowledge of Data Engineering and Analytics fundamentals — data modeling, ELT/ETL pipeline design, distributed compute (Spark/Databricks).
* Strong knowledge of deploying AI/ML in production — feature engineering, model selection, deployment, and ML-Ops.
* Strong communication and…