Lead Data Engineer

Primary Responsibilities

• Own the technical direction, architecture, and ongoing evolution of our Snowflake data warehouse, with accountability for performance, scalability, security, reliability, and cost efficiency. 

• Lead the design, development, and standardization of scalable ETL/ELT pipelines across data sources and destinations, including the continued evolution toward Azure Data Factory. 

• Establish and uphold engineering standards for code quality, testing, documentation, version control, deployment, monitoring, and data quality. 

• Lead the design and development of dimensional data models, including star schemas within a medallion architecture, using tools such as dbt, Sigma, and related technologies. 

• Partner with technical and business stakeholders to translate data needs into a prioritized technical roadmap and deliver trusted, reusable data solutions that support decision-making across the company. 

• Provide technical guidance through solution design and code reviews, mentor data engineers and analysts, and help the team resolve complex technical challenges. 

• Evaluate and introduce tools, patterns, and process improvements that strengthen scalability, reliability, maintainability, and delivery efficiency. 

• Lead complex and ad hoc data engineering initiatives from requirements through delivery while promoting data governance, data literacy, and responsible data practices across the organization. 

Desired Background

• Bachelor's degree in computer science, Information Technology, Engineering, or a related field. 

• 5+ years of progressive experience in data engineering, including ownership of complex initiatives and technical leadership within a modern cloud data environment. 

• Advanced hands-on experience with Snowflake, including data warehouse architecture, performance tuning, security and access controls, and cost optimization. 

• Advanced SQL skills and strong experience with a scripting language such as Python for data processing, automation, and troubleshooting. 

• Strong experience designing and operating scalable ETL/ELT pipelines, including orchestration, testing, monitoring, and production support. 

• Hands-on experience with dimensional data modeling, star schema and medallion architectures, and dbt or a comparable transformation framework. 

• Experience with Azure Data Factory or a comparable cloud orchestration platform; experience modernizing or migrating legacy data pipelines is preferred. 

• Demonstrated technical leadership through mentoring, design and code reviews, engineering standards, and cross-functional delivery, with strong communication and prioritization skills.