Senior Data Engineer, EvenUp

• 4+ years of experience as a Data Engineer or Software Engineer, ideally in product-focused, high-growth environments
• Strong proficiency in Python for data wrangling, orchestration logic, and navigating application codebases
• Strong proficiency in SQL and experience using transformation tools such as dbt to build reusable data models
• Experience designing dimensional models and defining clear, trusted business metrics
• Experience building scalable, reliable data pipelines in modern cloud environments
• Familiarity with data warehouse technologies such as BigQuery or Snowflake and orchestration tools such as Airflow or Dagster
• Experience enabling reporting and visualization solutions using dashboarding tools (e.g., Looker, Tableau, Metabase)
• Strong communication and collaboration skills when working with cross-functional stakeholders
• High attention to detail, structured thinking, and a bias toward action in fast-paced environments
• Leadership potential with interest in mentoring others and shaping data engineering best practices

Desirable

• The Data & Analytics team at EvenUp plays a critical role in driving data-informed decision-making across the organization. We partner closely with Product, Engineering, Operations, and Executive teams to uncover insights, build scalable data solutions, and enable a culture of data-driven decision-making and continuous improvement
• Our team owns the full data lifecycle — from foundational data modeling and reporting to advanced analytics, experimentation, and forecasting. We’re focused on building a scalable, reliable data platform that powers fast, actionable insights across the business
• Build and scale the data platform that powers analytics, experimentation, and business intelligence across the organization
• Design and maintain robust, well-documented ELT/ETL pipelines and reusable data models
• Partner cross-functionally with Product, Engineering, Operations, and Analytics teams to translate business needs into scalable data solutions
• Own the design and implementation of data pipelines supporting product instrumentation, reporting, and experimentation
• Establish and maintain high standards for data quality, reliability, governance, and observability
• Contribute to a strong data engineering culture through mentorship and best practices
• Data Platform Development: Build and maintain scalable data pipelines and infrastructure using tools such as dbt, Airflow or Dagster, and BigQuery, Snowflake, or similar technologies. Ensure reliability, documentation, and performance at scale
• Cross-Functional Collaboration: Partner closely with Product Managers, Data Scientists, ML Engineers, Software Engineers, and Operations stakeholders to understand data requirements and deliver high-quality, scalable solutions
• Data Modeling & Architecture: Design dimensional models, define clear source-of-truth metrics, and drive improvements in data architecture with a focus on usability, governance, and scalability
• Data Quality & Observability: Improve data reliability by implementing testing, monitoring, and observability best practices to reduce human error and ensure trust in data outputs
• Analytics & Reporting Enablement: Support business intelligence and experimentation efforts by building data models and enabling reporting and dashboarding solutions using tools such as Looker, Tableau, or Metabase
• Team Growth & Mentorship: Mentor engineers and analysts on data modeling standards, tooling, and engineering best practices while helping shape a strong, collaborative data culture