IT Principal Data Engineering

Save A Lot is seeking a Principal Data Engineer to lead the design, development, and operation of platforms and pipelines that support their data science capabilities. This role involves a combination of data engineering and data science, requiring collaboration with various teams to ensure reliable data flow and effective AI/ML implementation.

Responsibilities:

• 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
• 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
• 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
• 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
• 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
• 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

Requirements:

• 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
• 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