Principal Data Engineer

Geode Capital•Boston, MA

8d•Hybrid

About The Position

Geode Capital Management, LLC is seeking a full-time Principal Data Engineer to join our Data team. Reporting to the Director of Data Engineering, this highly visible role will lead the design and evolution of our enterprise data platform, shape data strategy in partnership with cross-functional stakeholders and drive large-scale data engineering initiatives that power advanced analytics, AI, and business intelligence across the organization. The ideal candidate combines deep technical expertise with strong leadership skills and a proven track record of building scalable, reliable, and secure data solutions. This is a unique opportunity for a hands-on leader who is excited to contribute to our current technology stack while helping define and build the next generation of our data platform. This role is based in our Boston, Massachusetts office. Employees are expected to follow the firm's in-office attendance policy, which currently requires in-office attendance on Tuesday, Wednesday, and Thursday, with the option to work remotely on Monday and Friday. Please note that Geode may modify this policy at any time.

Requirements

Bachelor’s or Master’s degree in Computer Science or a related field.
10+ years of experience in Data Engineering, Analytics Engineering, or ML related roles.
Experience in a Principal, Lead, or Architect role with strong Python or Java programming skills.
Proven ability to set technical standards, influence cross-functional teams, and drive long-term architecture strategy.
Deep expertise in Python, Airflow, DBT, advanced SQL, ETL/ELT design and data modeling.
Extensive experience building and supporting production-scale data platforms, data pipelines, and ML workflows
Strong hands-on experience enabling AI/ML solutions for analytics use cases.
Strong expertise in Data warehousing with Snowflake

Responsibilities

Translating complex business problems into high-value analytics and AI solutions
Architect and guide the development of AI/ML solutions supporting analytics use cases
Design scalable, secure, and resilient data ecosystems supporting operational, analytical, and AI/ML workloads.
Establish engineering standards, best practices, frameworks, and governance policies for data management and Python-based data and ML development
Ensure analytics and AI models are scalable, explainable, monitored, and aligned with enterprise analytics needs
Partner with Product, Analytics, and Business stakeholders to define data, feature, and model requirements
Collaborate closely with Data Science leadership and teams to bridge experimentation into reliable production-grade ML systems
Establish and oversee model lifecycle practices, including validation, retraining, and performance monitoring
Mentor engineers fostering engineering excellence and innovation
Evaluate emerging technologies and recommend innovative solutions to enhance data capabilities.