Data Engineer

Cleerly•New York, NY

7d•$160,000 - $174,000•Remote

About The Position

Cleerly is revolutionizing how heart disease is diagnosed, treated, and tracked. We are a growing team of world-class engineering, operations, medical affairs, marketing, and sales leaders. We raised $223M in Series C funding in 2022 and an additional $106M in a Series C extension in December 2024, enabling rapid growth and continued support of our mission. Most of our teams work remotely and have access to our offices in Denver, Colorado, New York, New York, Dallas, Texas, and Lisbon, Portugal, with some roles requiring on-site presence. Cleerly has created a new standard of care for heart disease through value-based, AI-driven precision diagnostic solutions aimed at preventing heart attacks. Our technology quantifies and characterizes atherosclerosis (plaque buildup) in heart arteries, going beyond traditional measures. Our solutions are supported by over a decade of clinical trials identifying risk factors beyond symptoms. We collaborate digitally using Google Workspace, Slack, Confluence/Jira, and Zoom Video. Travel is required for some team meetings and cross-functional projects, typically once per month or quarter. Cleerly is committed to providing safe and effective medical software, adhering to regulatory requirements and setting measurable quality objectives. Continuous improvement, risk management, and ongoing compliance are critical to achieving Cleerly's quality objectives.

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 CloudWatch - 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

Nice To Haves

Prior experience with Google Workspace (GMail, Drive, Docs, Sheets, Slides), Slack, Confluence/Jira, and Zoom Video is a plus.

Responsibilities

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