Data Analyst — Boston, MA

About The Role

The Data Analyst sits at the intersection of data infrastructure and business strategy, turning raw event streams, product telemetry, and operational data into analyses that directly shape roadmap and go-to-market decisions.

This role goes beyond dashboards - the analyst owns the semantic layer, partners closely with analytics engineers on data model design, and is expected to defend metric definitions across product, growth, and finance teams.

Key Responsibilities

• Own and evolve core business metrics (activation, retention, revenue funnels) by defining clear, documented definitions in dbt and enforcing them across the organization

• Write advanced SQL and Python analyses against a Snowflake warehouse, working with datasets in the hundreds of millions of rows

• Build and maintain executive-facing dashboards in Looker (or Tableau), with an emphasis on self-serve tooling over ad-hoc requests

• Partner with analytics engineers on dimensional modeling and data pipeline design, flagging data quality issues and driving fixes upstream

• Design and analyze A/B tests end-to-end, including experiment framing, power analysis, and statistical interpretation with the data science team

• Deliver deep-dive analyses - cohort retention, unit economics, funnel drop-off - with clear narratives and actionable recommendations for senior leadership

• Serve as an analytics point of contact for product managers and marketers, translating ambiguous business questions into rigorous, scoped analysis

What We Are Looking For

• 3–6 years of experience in data analytics, business intelligence, or a closely related role at a product-driven technology company

• Expert-level SQL, including window functions, CTEs, and query optimization on large-scale analytical warehouses (Snowflake, BigQuery, or Redshift)

• Strong working proficiency in Python or R for analysis, including pandas and standard visualization libraries

• Hands-on experience with a modern BI tool (Looker, Tableau, or Power BI) and an understanding of semantic layer best practices

• Solid grounding in statistics: hypothesis testing, regression, experiment design, and common analytical pitfalls

• Bachelor's degree in a quantitative field (statistics, economics, computer science, engineering) or equivalent practical experience

• Bonus: experience with dbt, event tracking tools (Segment, Amplitude), and familiarity with data modeling frameworks like Kimball or dbt best practices