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# Principal Data Engineer

- **Company:** Geode Capital Management
- **Location:** Boston, MA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $170,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Data Analysis, Information Engineering, Extract Transform Load (ETL), Dataspaces, Data Warehousing, Python (Programming Language), Machine Learning, Sql Optimization, Snowflake, Information Technology, Performance Monitor, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** July 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5c634cb80b66e4e9

## About the Role

* 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

## Description

* 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.


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