Principal Data Engineer

Role Description

We are looking for a Principal Data Engineer to sit at the intersection of data engineering and applied data science. You will own the design, development, and operation of the platforms and pipelines that power our data science capabilities — ensuring data flows reliably from source systems through to analysis, and business consumption. This role is roughly 75% data engineering and 25% data science and is ideal for someone who builds with engineering rigor but thinks with a data science mindset; someone who is energized by building platforms that make AI real in an organization. The right candidate is curious by nature — you explore out-of-the-box ideas and stay current with the fast-moving AI/Machine Learning (ML) landscape.

You’ll work directly with:

• Business analysts
• Product owners
• Business end-users
• Engineering and application teams
• Data/platform engineering teams

A consultative communication style is critical as shared outcomes across technology and business are the expectation.

Qualifications

• Bachelor’s degree in computer science, engineering, mathematics, or a related field, OR 7+ years of equivalent verifiable experience, skillset, and record of accomplishment
• Experience in a Principal or Senior Data Engineer role with direct involvement in ML platform or Data Science work
• Proficiency in an analytics/BI tool such as Power BI

Requirements

• Modern data stack technologies — Databricks (strongly preferred), Snowflake, Spark
• Inbound/outbound transportation of data with APIs and FTPs
• MPP databases such as Databricks, Snowflake, BigQuery, Teradata, or Azure Synapse
• Cloud platforms — AWS, Azure, or GCP
• Python and SQL
• Building and deploying ML models (classification, regression, forecasting, NLP, or similar)
• Familiarity with ML frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow
• MLflow or similar tools for experiment tracking, model registry, and deployment
• Understanding of feature engineering, model evaluation, and common ML failure modes
• Strong understanding of data modelling techniques (Kimball, Data Vault) and distributed systems
• Familiarity with feature stores, training pipelines, and batch/real-time inference architectures

Benefits

• 401K company match up to 4%
• Paid Time Off
• Medical Insurance options including FSA & HSA
• Vision Insurance
• Dental insurance
• Employee Assistance Programs
• Team Member Referral Program
• Tuition Reimbursement
• Wellbeing Program
• Career development opportunities