Principal Data Engineer

Boston, MA | Hybrid (3 Days Onsite)

We are seeking a Principal Data Engineer to lead the architecture and evolution of a next‑generation enterprise data platform that powers advanced analytics, AI/ML, and business intelligence across the organization. If you are interested in a highly visible technical leadership role for a hands‑on architect who thrives on solving complex data challenges at scale.

Please reach out to Chris McMillan at [email protected]

The Role

• Lead the architecture and technical direction of the enterprise data platform.
• Design scalable, secure, and resilient data ecosystems supporting analytics, operational reporting, and AI/ML workloads.
• Architect modern data pipelines, data warehouses, and machine learning infrastructure.
• Partner with Product, Analytics, and Data Science teams to translate complex business challenges into scalable data and AI solutions.
• Establish engineering standards, governance, and best practices for data engineering and Python-based development.
• Design production‑grade ML workflows, including model deployment, monitoring, validation, and retraining strategies.
• Drive enterprise data architecture, data modeling, ETL/ELT design, and platform modernization initiatives.
• Evaluate emerging technologies and identify opportunities to leverage AI and automation across the data platform.
• Mentor senior engineers and influence engineering excellence across multiple teams.
• Serve as the technical authority for enterprise data architecture and long‑term platform strategy.

Experience

• 10+ years of experience in Data Engineering, Analytics Engineering, or Machine Learning Engineering.
• Experience as a Principal Engineer, Data Architect, or Technical Lead owning enterprise-scale data platforms.
• Expert-level proficiency in Python, advanced SQL, dbt, Apache Airflow, and modern ETL/ELT architectures.
• Extensive experience designing and supporting large-scale Snowflake data warehouse environments.
• Deep expertise in data modeling, distributed data processing, and cloud-native architecture.
• Proven strong understanding of data governance, security, scalability, and engineering best practices.
• Demonstrated ability to define technical strategy, establish architectural standards, and influence engineering organizations.
• Snowflake architecture and optimization
• AI/ML platform architecture and MLOps
• Cloud-native data platforms (AWS and/or Azure)
• Data governance and enterprise architecture
• CI/CD and Infrastructure as Code
• Experience leading cross-functional engineering initiatives and mentoring senior engineers