Data Engineer

Requirements

• 5+ years of experience in a data engineering or analytics engineering role
• Strong proficiency in SQL and hands‑on experience with Snowflake, including warehouse management, query optimization, and data modeling patterns
• Experience with dbt (Core or Cloud) for building and maintaining modular, tested transformation pipelines
• Demonstrated ability to work closely with business stakeholders to define and implement metrics and data definitions — not just build pipelines
• Solid understanding of dimensional modeling, data vault, or other warehouse modeling approaches
• Experience with workflow orchestration tools such as Airflow, Dagster, or Prefect - you'll be creating and improving the DAGs that drive our Snowflake pipelines end to end
• Hands‑on experience with AWS data services - S3, MWAA, and Cloud Watch - supporting production data pipelines in a cloud‑native environment
• Ability to work through ambiguity and exercise good judgment when translating loosely defined business requirements into structured data models
• Bachelor's degree in Computer Science, Engineering, Math, or a related field, or equivalent practical experience

• The BI & Data team at Cleerly provides in-depth analysis that puts data and insights at the heart of decision making. We are a team of engineers, product managers, and analysts located across the U.S. with a passion for data architecture, visualization, and analysis
• Own the design, development, and maintenance of data models within Snowflake that serve as the foundation for business metrics, reporting, and analytics across the organization
• Partner closely with business stakeholders to translate operational definitions and KPIs into reliable, well‑documented data structures
• Build and maintain dbt models that power our core metrics layer, ensuring accuracy, consistency, and reusability across teams
• Manage and evolve our Snowflake data warehouse, including schema design, performance optimization, and data organization best practices
• Collaborate with analytics and BI teams to ensure the data layer supports self‑serve reporting and business review cadences
• Contribute to data modeling standards and best practices that allow the team to scale with confidence
• Participate in strategic discussions around data architecture and help evaluate trade‑offs across build vs. buy decisions