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.
Requirements
- 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.
- 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.
- 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).
- Substantial 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.
- 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.
Benefits
- Competitive salaries
- Opportunities for growth
- Tuition reimbursement
- 401k matching
- Medical, Dental, Vision and Life Insurance
- Additional wellness support
