LandOffer
Data Analyst
Job details
- Experience
- 3+ years
- Education
- Bachelor's degree
- Posted
- Oct 23, 2026
- Last confirmed open
- Oct 23, 2026
About this role
About The Role
The role sits at the center of the company's data ecosystem, turning raw product, revenue, and operational data into analyses that drive roadmap and go-to-market decisions. This is not a dashboard-maintenance position - the analyst owns problems end-to-end, from defining metrics with stakeholders to building the SQL and dbt models that make those metrics reliable.
You will work closely with analytics engineers, data engineers, and business leaders in a modern data stack environment (Snowflake, dbt, Looker), where your work directly shapes pricing, product prioritization, and growth strategy.
Key Responsibilities
Own core business metrics end-to-end: define, instrument, validate, and monitor KPIs across product usage, retention, and revenue Build and maintain dbt models and SQL transformations in Snowflake, ensuring data quality and consistent metric definitions across the organization Design and develop dashboards in Looker (or similar BI tools) that leadership and cross-functional teams actually use to make decisions Run deep-dive analyses - cohort retention, funnel analysis, pricing experiments, A/B test evaluation - and present findings with clear recommendations Partner with product managers and growth teams to translate ambiguous business questions into rigorous, testable analytical frameworks Identify data quality issues upstream, work with data engineers on root causes, and build automated tests to catch regressions early Contribute to the team's documentation, metric dictionaries, and analytical best practices What We Are Looking For 3-6 years of experience in data analytics, business intelligence, or a comparable analytical role at a product-driven company Expert-level SQL - complex joins, window functions, CTEs, performance tuning - and the ability to work with large, messy datasets independently Hands-on experience with dbt (or similar transformation tooling) and a cloud data warehouse such as Snowflake, BigQuery, or Redshift Proficiency in Python or R for ad-hoc analysis, statistical testing, and lightweight modeling; comfort with pandas is expected Experience with a modern BI tool (Looker, Tableau, Power BI) including semantic layer or metric definition work Track record of communicating technical findings to non-technical stakeholders and influencing decisions with data Bachelor's degree in a quantitative field (statistics, economics, CS, engineering) or equivalent practical experience.
Bonus: experience with experimentation platforms and causal inference methods, exposure to Airflow or other orchestrators, or prior work in a B2B SaaS environment