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Roles
Compensation
USD 182300 - 291500
Pay Range $182,300.00 - $291,500.00
- Salary period
- yearly
- Location basis
- Unclear
Tech stack
dbt
Sagemaker
Snowflake
Databricks
Dagster
Kafka
Python
DataHub
Looker
AWS
Hightouch
Git
SQL
Fivetran
Tableau
Apache Airflow
Claude Code
Cursor
Census
Required
SnowflakedbtDagsterDataHubTableauClaude CodeCursorPythonSQLKafkaGit
Nice to have
AirflowDatabricksSageMakerFivetranHightouchCensusLooker
Location
Waltham, MA, Newton, MA
Work setup
- Employment
- full-time
- Level
- Senior
- Remote policy
- Unclear
- Remote scope
- onsite
- Travel
- Unclear
- Relocation
- Location: Waltham, MA (moving to Newton, MA within the next month)
- Visa
- Unclear
- Authorization
- Unclear
Role details
Responsibilities
- Lead the data platform (Snowflake, dbt, Dagster, DataHub), team, and strategy.
- 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 archiecture 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.
- 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.
- 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.
Application
- Portfolio
- unclear
- GitHub
- not required
- Cover letter
- unclear
- Apply flow
- ats
Company context
- Product
- fuel distribution, terminals, retail
- Industry
- energy
Description
Hiring a Senior Director, Data Engineering to lead our data platform (Snowflake, dbt, Dagster, DataHub), team, and strategy. Actively hiring.
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