Senior Director, Data Engineering

Salary: $182,300 - 291,500 per year Requirements:

• We require 12+ years of experience in Data Engineering, Analytics Engineering, or Data Platform leadership, including at least 7 years in senior management roles.
• We require at least 6 years of experience leading, mentoring, and developing technical teams, including managing managers.
• We require a bachelors or masters degree in a quantitative discipline such as Computer Science, Mathematics, Statistics, Engineering, Physics, or Economics.
• We look for experience working in a federated hub-and-spoke data organization that supports both centralized platforms and embedded business-unit analytics teams.
• We need expert-level knowledge of the modern data stack, with strong hands-on proficiency in Snowflake, dbt, Dagster or Airflow, and cloud lakehouse patterns.
• We expect working knowledge of Databricks or SageMaker, Fivetran, Hightouch or Census, DataHub or similar catalog and observability tools, Tableau, and Git-based workflows.
• We require strong fluency with cloud infrastructure, preferably AWS, plus infrastructure as code, containerization, and modern CI/CD.
• We look for proven experience designing and operating production data systems with formal data contracts, SLAs, lineage, and observability.
• We require practical understanding of streaming and near-real-time architectures such as Kafka, Snowflake Dynamic Tables, and change data capture.
• We need demonstrated FinOps capability for cloud data platforms, including attribution, governance, and ongoing optimization of Snowflake and related compute spend.
• We expect experience integrating AI-assisted and agentic development tools such as Claude Code, Cursor, MCP servers, and shared skills or tools repositories into engineering workflows.
• We require knowledge of how to design, evaluate, and govern LLM- and agent-based features in production, including evals, human-in-the-loop patterns, prompt and skill versioning, and observability.
• We look for familiarity with AI/ML data infrastructure such as feature stores, vector databases, RAG retrieval pipelines, embeddings management, and model or agent monitoring.
• We need strong experience with software engineering best practices and agile delivery, including sprint planning, code review, testing, CI/CD, on-call, and postmortems.
• We require substantial experience partnering with product management, including stakeholder alignment, roadmap negotiation, ROI analysis, and synthesizing multiple viewpoints into a coherent plan.
• We need proficiency in Python and SQL, with comfort reading and reviewing code across the teams primary languages.
• We look for familiarity with BI and analytics tooling such as Tableau, Looker, or equivalent.
• We expect a working understanding of statistical methods and ML fundamentals so we can engage effectively with data science partners.
• We want strong leadership, interpersonal, written, and verbal communication skills, with the ability to explain complex technical and AI strategy to executive and Board-level audiences.
• We need someone comfortable operating in ambiguity, leading through change, and standing up new teams, capabilities, and ways of working.

• We define, execute, and evolve our enterprise data and platform strategy so it supports scalable, reliable, governed, and cost-aware solutions.
• We own the multi-year roadmap for our core data platform, including a path toward streaming, real-time activation, data mesh architecture, and AI/ML enablement.
• We lead data engineering strategy for new business areas, M&A integrations, and adjacent revenue opportunities.
• We establish data engineering as a measurable contributor to company performance, including uptime, time-to-insight, decision quality, and operating margin.
• We champion and operationalize agentic development as our standard way of working across data engineering, DSML, and embedded teams.
• We build and govern our internal AI tooling layer for data work, including agent-assisted development, automated lineage and documentation, AI-driven code review, agentic data quality, and incident triage.
• We partner with DSML to provide the data and platform foundations for AI/ML products, including feature stores, vector stores, RAG infrastructure, evaluation tooling, and model or agent observability.
• We set engineering guardrails for safe and reliable use of LLMs and agents in production workflows, including human-in-the-loop patterns, evaluations, versioning, and audit trails.
• We own our dbt layer conventions, data contracts, SLAs, and single source of truth discipline.
• We lead the engineering side of master data management and help ensure governed, conformed enterprise dimensions.
• We drive data governance across security, privacy, access control, lineage, and compliance, embedding checks into the development process.
• We lead modernization initiatives that enable real-time and near-real-time operations across terminals, retail, and supply and trading.
• We own platform FinOps, including visibility, attribution, and continuous optimization of compute, storage, and AI inference spend.
• We lead and grow our central Data Engineering function within the federated DAI organization, supporting both core platforms and embedded BI teams.
• We develop strategies to build high-performing data engineering teams and foster a culture of innovation, collaboration, curiosity, ownership, and data-driven decision-making.
• We oversee team operations and engineering practices such as agile delivery, sprint planning, code review, CI/CD, testing, on-call, postmortems, and continuous improvement.
• We mentor senior engineers and engineering managers and build a pipeline of future technical leaders.
• We build cross-functional partnerships with Data Science/ML, Central Analytics, Technical Product Management, IT, Cybersecurity, and business units to drive data and AI innovation.
• We lead data engineering workstreams tied to major corporate initiatives and digital transformation programs, including our multi-year AI strategy and federated DAI buildout.
• We work with stakeholders across Operations, Retail, Supply and Trading, Finance, and HR to turn business needs into data products and technical requirements with clear ROI.
• We communicate platform strategy, progress, risks, and impact to diverse stakeholders, including senior executives and Board-level audiences when needed.

• AI
• Airflow
• AWS
• Census
• CI/CD
• Claude Code
• Cloud
• Cursor
• Databricks
• Embedded
• Fivetran
• Git
• Hightouch
• Kafka
• LLM
• Looker
• Marketing
• MCP
• Python
• RAG
• SQL
• Security
• Snowflake
• Tableau
• dbt
• Support

More:

We are Global Partners, a Fortune 500 company with more than 90 years of experience, and we believe business starts with people. Since 1933, we have focused on taking care of our customers, guests, communities, and one another while fueling communities responsibly and sustainably. We offer competitive pay, health and wellness coverage, 401(k) with a match, tuition reimbursement after six months of service, and professional development resources for long-term growth. This Senior Director of Data Engineering role sits within our federated Data, Analytics & Insights organization and supports our enterprise data platform, embedded analytics teams, and broad digital transformation agenda across terminals, retail convenience stores, fuel marketing, and supply and trading. We hire virtually and in person depending on the role, and we foster an inclusive culture where diverse perspectives help us grow, move, and thrive.

last updated 35 week of 2026