Senior Director, Data Engineering

Global Partners LP · Waltham, MA · 1 wk ago

EngineeringFull-time

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.

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.

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.

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.

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.
Prominent 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.

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.

Extensive experience with software engineering best practices and agile delivery (sprint planning, code review, testing, CI/CD, on-call, postmortems).
Sizable experience partnering with product management — stakeholder management, roadmap negotiation, ROI reasoning, and synthesizing diverse viewpoints into a coherent plan.
Proficiency in Python and SQL; comfortable reading and reviewing code across the team’s primary languages.
Familiarity with data analysis and BI tooling (Tableau, Looker, or equivalent) sufficient to partner credibly with analytics consumers.
Working understanding of statistical methods and ML fundamentals; able to engage substantively with data science partners.

A passion for data quality, systems design, and developer experience, coupled with curiosity about how teams and systems succeed and grow.
Strong leadership and interpersonal skills; capable of building and maintaining trusted relationships across departments and fostering a collaborative, empowering management style.
Excellent written and verbal communication, with the ability to articulate complex technical and AI strategies to executive and Board-level audiences.
Comfort operating with ambiguity and leading through change, including standing up new teams, capabilities, and ways of working.

We value passion and potential. Please apply if you’re qualified and interested—we’d love to hear from you.
A member of our Talent Acquisition team will review your application and may connect you with the hiring manager if your experience is a strong match.
Interviews are conducted virtually and in person, depending on the role. We’ll provide more details about next steps if selected to move forward.

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