Senior Director, Data Engineering

Senior Director Of Data Engineering

Join Global Partners as Senior Director of Data Engineering to lead the architecture, development, and optimization of our enterprise data platform. This role drives the design and implementation of modern, AI-native data infrastructure that powers analytics, operations, real-time decisioning, and digital transformation across our energy value chain terminals, retail convenience stores, fuel marketing, and supply and trading. It is a pivotal role in enabling data-driven decision-making and AI-enabled innovation across the organization. Your mission extends beyond conventional data engineering. You will lead a team that operates at the modern data stack Snowflake, dbt, Dagster, DataHub, and a maturing portfolio of streaming, ML, and agentic capabilities while embedding agentic development practices (Claude Code, Cursor, MCP-based tooling, AI-assisted code review and data quality) as the default way our engineers, analytics engineers, and embedded BI teams build and ship. You will translate complex business questions into durable technical strategy, set the platform direction that the central Data, Analytics & Insights (DAI) organization and embedded BU analytics teams build on, and serve as the senior technical voice partnering with Data Science/ML, Central Analytics, Technical Product Management, IT, and business leadership. This role is tailor-made for a leader who finds their passion at the intersection of data, AI, and operational impact, and who wants to help define what a federate data organization looks like at a Fortune 500 energy company.

At Global Partners, business starts with people. Since 1933, we've believed in taking care of our customers, our guests, our communities, and each otherand that belief continues to guide us.

The Global Spirit is how we work to fuel that long term commitment to success. As a Fortune 500 company with 90+ years of experience, we're proud to fuel communitiesresponsibly and sustainably. We show up every day with grit, passion, and purposeanticipating needs, building lasting relationships, and creating shared value.

Job Description

Strategy & Platform Direction

• Define, execute, and evolve a forward-thinking enterprise data and platform strategy aligned with Global Partners' long-term objectives, ensuring scalable, reliable, governed, and cost-aware data solutions.
• Set and own the multi-year roadmap for the core data platform (Snowflake, dbt, Dagster, DataHub, Tableau, and adjacent ML/AI infrastructure), including a credible path to streaming, real-time activation, data-mesh architecture and AI/ML enablement.
• Lead data engineering strategy for expansion into new business areas, M&A integrations, and adjacent revenue opportunities (e.g., new fuel products, retail loyalty, mobility, sustainability reporting).
• Establish data engineering as a measurable driver of company performance uptime, time-to-insight, decision quality, and operating margin contribution.

Agentic & AI-Assisted Engineering

• Champion and operationalize agentic development as the default way the team builds: standardize development conventions, shared skills/tools repositories, and MCP-based integrations across Data Engineering, DSML, and embedded teams.
• Build and govern the internal AI tooling layer for data work agent-assisted development, automated lineage and documentation, AI-driven code review, agentic data quality and incident triage, and natural-language interfaces to the warehouse.
• Partner with the DSML team to provide the data and platform foundations for AI/ML products, including feature store, vector store, RAG retrieval infrastructure, evaluation tooling, and model/agent observability.
• Establish the engineering guardrails for safe, reliable use of LLMs and agents in production data workflows including human-in-the-loop patterns, evals, prompt and skill versioning, and audit trails.

Data Platform, Quality & Governance

• Own the integrity of the dbt layer conventions (RAW ? CUR ? BTR ? APP), data contracts, SLAs, and the Single Source of Truth (SSOT) discipline that downstream BUs depend on.
• Lead the engineering side of MDM, partnering with the implementation and downstream consumers to ensure governed, conformed dimensions across the enterprise.
• Champion robust data governance security, privacy, access control, lineage, and compliance and embed these as automated, shift-left checks rather than after-the-fact reviews.
• Lead initiatives to modernize core data systems for real-time and near-real-time business operations across terminals, retail, and supply/trading.
• Own platform FinOps: visibility, attribution, and continuous optimization of data platform compute, storage, and AI/inference spend.

Organization & Talent

• Lead and grow the central Data Engineering function within the federated DAI organization, supporting both centrally-owned platforms and the embedded BI teams across business units.
• Develop strategies for building world-class data engineering teams fostering a culture of innovation, collaboration, curiosity, ownership, and data-driven decision-making.
• Oversee team operations and engineering practice: agile delivery, sprint planning, code review, CI/CD, testing, on-call, postmortems, and continuous improvement of the SDLC.
• Mentor senior engineers and engineering managers; build a deliberate pipeline of technical leaders fluent in both modern data stack and AI-augmented development.

Cross-Functional Leadership

• Establish cross-functional alliances to drive data and AI innovation in partnership with Data Science/ML, Central Analytics, Technical Product Management, IT, Cybersecurity, and the business units.
• Lead the data engineering aspects of major corporate initiatives and digital transformations, including the multi-year AI strategy and the federated DAI buildout.
• Collaborate with stakeholders across Operations, Retail, Supply & Trading, Finance, and HR to translate complex business needs into data products and technical requirements with clear ROI.
• Build strong, influential partnerships across the organization, driving adoption of the enterprise data strategy and the AI-assisted ways of working.
• Effectively communicate platform strategy, progress, risks, and impact to diverse stakeholders including the COO, CPO, CEO, and Board-level audiences when called on.

Additional Job Description

Experience

• 12+ years of experience in Data Engineering, Analytics Engineering, or Data Platform leadership, with a minimum of 7 years in senior management roles.
• At least 6 years leading, mentoring, and developing technical staff in a dynamic, innovative environment, including managing managers.
• Bachelor's or Master's degree in a quantitative field such as Computer Science, Mathematics, Statistics, Engineering, Physics, or Economics.
• Demonstrated experience operating in a federated (hub-and-spoke) data organization, supporting both centrally-owned platforms and embedded business-unit analytics teams.

Modern Data Stack & Platform

• Expert-level technical knowledge of the modern data stack, with deep proficiency in Snowflake, dbt, Dagster (or Airflow), and a cloud lakehouse pattern; working knowledge of Databricks/SageMaker, Fivetran, Hightouch/Census, DataHub (or comparable catalog/observability), Tableau, and Git-based workflows.
• Strong fluency with cloud infrastructure (AWS preferred), infrastructure-as-code, containerization, and modern CI/CD.
• Proven track record of designing and operating production data systems with formal data contracts, SLAs, lineage, and observability.
• Hands-on understanding of streaming and near-real-time architectures (e.g., Kafka, Snowflake Dynamic Tables, change data capture) and when to apply them.
• Demonstrated ability to manage cloud data platform cost (FinOps): attribution, governance, and continuous optimization of Snowflake and adjacent compute spend.

AI / Agentic Engineering

• Demonstrated experience integrating AI-assisted and agentic development tooling (e.g., Claude Code, Cursor, MCP servers, shared skills/tools repositories) into the day-to-day workflow of engineering and analytics teams.
• Practical understanding of how to design, evaluate, and govern LLM- and agent-based features in production including evals, human-in-the-loop patterns, prompt/skill versioning, and observability.
• Familiarity with the data infrastructure that supports AI/ML products: feature stores, vector databases, RAG retrieval pipelines, embeddings management, and model/agent monitoring.
• Comfortable setting standards for safe and effective use of AI in regulated, operationally critical environments.

Engineering & Delivery Practice