Analytics Engineer

Must-have

• A real passion for learning. You're willing to try things, make mistakes, fail fast, and grow from it. In our environment, the people who thrive are the ones who keep learning and aren't afraid to be wrong along the way
• 2+ years of hands-on analytics engineering with some visualization experience, using SQL and Python to build the tables that feed reporting. Experience with a BI tool (Looker, Power BI, Tableau, Zoho Analytics, or similar) helps.
• Strong SQL (CTEs, window functions, joins across messy real-world schemas) and comfort tracking down data quality issues in the source tables
• Working Python (pandas, requests) to pull data from an API or file, clean it, and load it into a database
• Experience transforming raw source data into clean, modeled tables for reporting
• Comfortable working directly with non-technical stakeholders and turning a vague ask ("why don't these numbers match?") into a clear, defensible metric
• Nice-to-have
• Experience scheduling, monitoring, or fixing data pipelines
• Experience embedding with a business team or department
• Familiarity with layered warehouse design (bronze/silver/gold) and dbt-style conventions
• Experience with a workflow orchestration tool
• What success looks like
• First 90 days You understand the business, the source systems, and the key reporting questions for your area You've delivered your first dashboard end to end, and the people it was built for are using it You've found and fixed at least one data quality issue at the source
• First year Leadership and the people working in the business rely on your reporting to run this part of the business Your data is current and trusted, and people stop asking "is this number right?" Your metrics and dashboards are documented so others can pick them up You can move data between systems on your own when needed
• Reports To Reports to the Director of Data. You'll be one of a small group of analytics engineers, each owning reporting for a specific part of the business rather than routing requests through the director. You'll handle light data engineering yourself and lean on a small data engineering team for anything bigger or upstream of your reporting layer. You'll also escalate data quality issues through our existing process instead of building one-off fixes around it.