CX Business Intelligence Analyst

Gusto, Inc.•Denver, CO

1d•$111,160 - $168,419•Hybrid

About The Position

Gusto is hiring a Business Intelligence Analyst for its Customer Experience (CX) team. The role owns the data layer supporting new service models, a telephony platform migration, and the data infrastructure behind these initiatives. The analyst partners directly with CX leaders to scope priorities, build trustworthy pipelines, data models, and dashboards, and use that foundation to investigate what drives customer outcomes. The analyst works within CX Data and Insights, the analytical backbone of Gusto’s Customer Experience organization. The team defines the metrics CX leaders use to run the business and produces analyses that inform operational roadmap decisions. The position combines hands-on coding and statistical analysis with judgment about which business questions to pursue. The role includes developing and scaling reporting, monitoring, and self-serve insights; applying statistical methods and AI-supported analytics; improving data quality and governance; and communicating findings to technical and non-technical audiences. It also contributes to team practices, tools, and the development of other data practitioners. Gusto has offices in Denver, San Francisco, and New York City. Employees based in those locations are expected to work in-office on designated days approximately two to three days per week, or more depending on the role. Employees approved to work elsewhere must have a secure, reliable, and consistent internet connection. Regular and predictable attendance is expected in all work arrangements.

Requirements

At least 3 years of experience in data analytics, data engineering, or data governance.
Strong SQL and Python proficiency, including the ability to query and analyze large datasets.
Strong foundation in data modeling, schema design, and data quality best practices, with functional experience on cloud platforms such as Snowflake.
Proven experience building and maintaining robust data reporting pipelines and ETL workflows.
Experience designing or operating governance for AI-assisted analytics outputs, including validation standards for AI-generated code and metric definitions, drift and source-of-truth audits for operational dashboards, and documented rules for when human review is mandatory for performance-adjacent data products.
Comfort working within an organization's AI use policy.
Clear, direct communication, including the ability to explain complex data investigations to non-technical stakeholders concisely.
Ability to work independently, manage competing priorities, and drive projects forward without complete requirements.
Frequent, practical use of AI in analytics workflows, with concrete examples of how it has affected pipeline scaffolding, code writing and review, data validation, or documentation, and an understanding of when human verification is essential.

Responsibilities

Own ambiguous CX data problems, design analysis and measurement frameworks, and establish practices for monitoring and enforcing data integrity.
Deliver multiple high-impact projects, balance trade-offs to maximize business value, and set clear expectations for deliverables and timelines.
Partner with CX leaders, cross-functional data science teams, tool developers such as Salesforce, and data engineering to align on strategy.
Apply advanced statistical methods and AI-driven anomaly detection to uncover opportunities, diagnose performance issues, and inform CX strategy.
Reconcile messy or conflicting data sources and establish reliable sources of truth.
Use AI to accelerate hypothesis generation, code drafting, cross-source reconciliation, and validation, and build agents to automate repetitive tasks.
Define DBT and Snowflake data pipeline requirements for evolving CX data sources.
Build and maintain monitoring systems and Streamlit dashboards for daily operations and self-serve insights.
Independently prioritize, create, and deliver data projects, proactively resolving conflicts or misalignment across stakeholders.
Improve team practices in data modeling and schema design, expand toolsets where they accelerate CX outcomes, and drive improvements in data quality, rigor, and adoption of data capabilities.
Mentor other data practitioners.
Present complex findings clearly and compellingly to technical and non-technical stakeholders, fostering a data-informed mindset across the company.