Senior Data Engineer

Senior Data Engineer page is loaded## Senior Data Engineer locations: SSC Irving TXtime type:
Full time posted on:
Posted Todayjob requisition : R26 _5752
* 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.*##

About This Opportunity##Build, test, deploy, and support reliable data pipelines, data products, and datasets that support reporting, analytics, AI/ML, and business operations. Work from architecture and business requirements to create production-ready solutions that are scalable, secure, cost efficient, and easy to support. Use approved AI tools to speed up coding, testing, troubleshooting, and documentation, and validate all generated work before production use.#

KEY RESPONSIBILITIES ## 1. Data Engineering
· Build, test, deploy, and support scalable data pipelines and datasets using Databricks, Spark, SQL, Python/PySpark, dbt, Airflow, or similar tools.
· Support batch and streaming data workloads, including ingestion, transformation, and consumption patterns for analytics and downstream systems.
· Build reliable data products for reporting, analytics, AI/ML, and business operations based on business and architecture requirements.
· Engineer Bronze, Silver, and Gold data layers in Databricks, following medallion architecture patterns where applicable.
· Build and maintain transformation pipelines using dbt, Spark-based frameworks, and approved engineering standards.
· Document solutions and improve pipeline performance, storage use, compute efficiency, and total cost to operate.## 2. Quality and Support
· Add automated tests, data-quality checks, monitoring, alerts, and clear error handling to improve data freshness, accuracy, and pipeline reliability.
· Troubleshoot production issues, find the cause, and implement lasting fixes that reduce repeat incidents.
· Support service levels and production readiness by making pipelines observable, testable, and easy to operate.
· Use source control, code review, CI/CD, and release practices.
· Participate in design and code reviews and share practical engineering practices with other engineers.## 4. Analytics and Self-Service Enablement
· Support semantic layers, aggregates, curated datasets, and performance improvements that help business users access trusted data.
· Partner with architecture, analytics, and product teams to make data products easier to use, reuse, and support.
· Support platforms such as Dataiku, Databricks, and Power BI where they are used for analytics and self-service delivery.## 5. AI-Assisted Engineering
· Follow access, encryption, privacy, retention, metadata, lineage, and stewardship requirements.
· Build integrations across internal systems, external data sources, and cloud platforms using secure and reliable patterns.
· Handle data in ways that meet company security, privacy, compliance, and audit expectations.
· Work with Security, Privacy, Governance, Architecture, and Platform teams when standards are unclear.## 4. AI-Assisted Engineering
· Use approved AI tools, including Windsurf, Devin, and Databricks Genie where appropriate, to speed up coding, testing, debugging, documentation, data analysis, and routine engineering work.
· Give AI tools clear context and requirements so generated code and recommendations fit the problem being solved.
· Review, test, and understand AI-generated code before it is…