Responsibilities
- Recruit, mentor, and develop a diverse team of engineers and data specialists, starting with a small core team that will grow to meet the evolving needs of the organization. Build capacity not only to maintain existing pipelines but also to develop and execute on a forward-looking vision for a scalable, world-class analytics platform
- Work with existing stakeholders to define and communicate a long-term strategy for analytics infrastructure and enablement, building a vision that solves real-world problems for our data scientists and extends toward a standardized, industry-class analytics platform
- Lead team execution in a complex, fast-paced research environment, reducing friction and optimizing workflows
- Influence laterally across engineering and research teams to align roadmaps, shape priorities, and drive shared goals
- Ensure robust communication of team progress, creating transparency and credibility with senior stakeholders
- Oversee consistent, well-documented, and queryable data models that power research today, and extend these frameworks toward the broader vision of a unified analytics platform
Requirements
- Bachelor’s degree in Computer Science or equivalent professional experience
- 3+ years of proven experience managing geographically distributed software engineering teams
- 3+ years of experience managing engineering or data teams focused on analytics or data infrastructure
- Demonstrated success managing through ambiguity, influencing without direct authority, and leading complex cross-team initiatives
- Exceptional stakeholder management and communication skills, capable of clearly articulating vision, progress, and impact
- Proven ability to attract, hire, mentor, and retain exceptional engineering talent
- Product-focused mindset, with experience shaping product vision, defining roadmaps, and delivering high-impact platform solutions
- Strong background in modern data infrastructure and modeling (e.g., SQL, Presto, Spark, Dask, Polars)
- Strong understanding of query optimization and performance tuning in SQL and experience with columnar storage formats (e.g., Parquet, ORC)
- Demonstrated experience designing normalized and denormalized data structures for analytical workloads
Preferred Qualifications
- Familiarity with AWS cloud technologies and on-prem compute clusters (e.g., Slurm, SSH, Unix)
- Familiarity with Airflow for data orchestration pipelines
- Exposure to quantitative research or machine learning environments
- Experience managing operationally critical, high-availability systems, ensuring precision, reliability, and robustness
- Expertise in metadata management, data lineage, and applying robust data governance principles
