IT Principal Data Engineering

Purpose 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, and our own data/platform engineering teams. A consultative communication style is critical as shared outcomes across technology and business are the expectation.   Responsibilities Platform Architecture & Strategy Define the long-term technical direction for the data science platform and integration with existing ELT pipelines Ensure platforms are scalable, reliable, secure, and cost-efficient at enterprise scale Evaluate and adopt emerging tools in the modern data and ML stack Data Engineering Development Design, develop, and optimize ETL pipelines and outbound data feeds Develop and follow templates and engineering patterns to reduce the time-to-deploy new data assets or changes to an existing data model or analytics solutions Partner with key business teams to understand their data needs and assist them in building appropriate data solutions to meet their business needs Data Science Development Design, build, and optimize end-to-end data science pipelines — from raw data ingestion through feature engineering, model training, and inference serving Contribute to MLOps practices including model versioning and monitoring, supporting the transition of data science work into production Technical Leadership & Mentorship Provide technical guidance to data engineers Conduct code reviews and champion engineering best practices across workstreams Lead without direct authority, influencing cross-functional teams across data engineering, analytics and product owners Data Governance & Quality Establish best practices for data quality, lineage, privacy, and security across data engineering and science pipelines Ensure model inputs and outputs are auditable, reproducible, and compliant with data governance standards Stakeholder Management Partner with data engineering, product owners, and software engineers to align platform capabilities with organizational AI/ML goals Translate complex technical concepts into clear, actionable insights for non-technical stakeholders   About You 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 Data Engineering experience: 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 ML & Data Science experience 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 Architecture experience Strong understanding of data modelling techniques (Kimball, Data Vault) and distributed systems Familiarity with feature stores, training pipelines, and batch/real-time inference architectures