Senior Analytics Engineer, Hims & Hers

• 8+ years of experience with SQL, preferably for data transformation or analytical use cases
• 4+ years of experience building scalable data models for analytical and BI purposes
• 3+ years of solid experience with dbt
• Mastery of data warehouse methodologies and techniques from transactional databases to dimensional data modeling, to wide denormalized data marts
• Solid experience with BI tools like Tableau and Looker
• Experience using version control (command-line, Git)
• Familiarity with at least one of the following data warehouses: Google BigQuery, Snowflake, Redshift, Databricks
• Domain expertise in one or more of the following business domains: Finance, Product, Marketing, Operations, or Customer Experience
• Demonstrated experience engaging and influencing senior leaders across functions, including an ability to communicate effectively with both business and technical teams
• Strong analytical and quantitative skills with the ability to use data and metrics to back up assumptions and recommendations to drive actions
• Ability to articulate vision, mission, and objectives, and tailor the narrative appropriately to the audience
• Experience working with management to define and measure KPIs and other operating metrics
• Bachelor’s or Master’s degree in Computer Science or related field
• Understanding of SDLC and Agile frameworks
• Project management skills and a demonstrated ability to work autonomously

Desirable

• We're looking for a savvy and experienced Senior Analytics Engineer to build seamless data products in collaboration with our data engineering, analytics, engineering, business, and product management teams
• Design and implement complex, scalable, and performant data models in our data warehouse (e.g., BigQuery, Databricks) using modern ELT tools (e.g., dbt)
• Collaborate with data engineers to build and maintain robust and scalable data pipelines that serve analytics and reporting needs
• Create clear and comprehensive documentation of datasets, metrics, and data flows to ensure data usability across teams
• Collaborate closely with analysts, data scientists, product managers, and business stakeholders to understand data needs, define quality standards, and take ownership of delivering high-impact data solutions
• Enforce data quality standards and develop comprehensive automated testing to ensure accuracy, consistency, and reliability of datasets
• Mentor junior engineers and analysts on best practices in data modeling, SQL, dbt, and analytics engineering principles and foster a culture of collaboration
• Build trust with internal stakeholders to encourage data-driven decision-making