Senior Data Analyst (Fully Remote)

PadSplit•Stratton, VT

1d•$45,000 - $55,000•Remote

About The Position

PadSplit is seeking a Senior Data Analyst to transform data into clear, trustworthy decisions. This role will own analyses end-to-end, build durable reporting infrastructure, and contribute to how the company measures its business. The analyst will utilize a modern data stack including Snowflake, dbt, and Hex to address high-impact questions and elevate the analytical standards within the team. The ideal candidate is comfortable with expert-level SQL, translating business questions into rigorous analysis, and communicating findings to both technical and non-technical audiences. They should possess curiosity, attention to detail, and a strong sense of ownership.

Requirements

5+ years of experience in data analytics or a closely related field.
Advanced skills in SQL, including window functions, CTEs, query optimization, and working with large, complex datasets.
Hands-on experience with Snowflake as a primary data warehouse.
Experience building and maintaining dbt models in a production analytics environment, including tests and documentation.
Experience building reporting in Hex or a similar notebook-based analytics platform.
A track record of owning analyses from scoping through delivery and follow-through.
Ability to explain technical findings to non-technical audiences and defend methodology.
Working familiarity with AI tools in analytics, paired with sound judgment on when to verify outputs and how to handle sensitive data.

Responsibilities

Write, optimize, and maintain complex SQL against Snowflake to answer business questions accurately and efficiently.
Build and maintain dbt models that transform raw data into well-documented, tested, and reusable datasets.
Build dashboards, apps, and reporting in Hex that leaders trust and use to run the business.
Partner with teams to define metrics, scope analyses, and translate ambiguous questions into clear analytical work.
Investigate anomalies in key metrics, identify root causes, and communicate findings with clear recommendations.
Maintain documentation across dbt and Hex to ensure metric definitions are consistent across teams.
Validate sources, pressure-test assumptions, and flag data gaps before they reach decision-makers.
Use AI tools to accelerate analysis and reporting while verifying outputs, and mentor other analysts on best practices.