Lead CX Business Intelligence Analyst

Gusto, Inc.•Denver, CO

3h•$160,000 - $200,000•Hybrid

About The Position

Gusto is hiring a Business Intelligence Analyst for its Customer Experience team to own the data layer supporting new service models, a telephony platform migration, and related data infrastructure. The analyst will work directly with CX leaders to identify and scope priorities, build trustworthy pipelines, data models, and dashboards, and use this foundation to answer causal questions about customer outcomes. The role is part of 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 analysis that shapes operational roadmap decisions. The analyst will lead ambiguous work, deliver multiple high-impact projects, set analytical and data-integrity practices, and partner with CX leaders, data science, tool developers, and data engineering. The position includes designing measurement frameworks; applying advanced statistical methods and AI-driven anomaly detection; reconciling messy data sources; defining DBT and Snowflake pipeline requirements; building monitoring systems and reporting; and improving dashboards and self-serve insights. The analyst will also advance data modeling and schema-design practices, evaluate tools such as Databricks, mentor data practitioners, and communicate findings to technical and non-technical stakeholders. Gusto expects team members to use AI tools in their work, and this role requires practical AI use alongside governance, validation, and human review of consequential analytics outputs.

Requirements

At least 5 years of experience in Data Analytics, Data Engineering, or Data Governance.
Strong proficiency in SQL and Python, including querying and analyzing large datasets.
Strong foundation in data modeling, schema design, and data quality best practices.
Functional experience working with 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 skills, including the ability to explain complex data investigations briefly to non-technical stakeholders.
Ability to work independently, manage competing priorities, and advance projects without waiting for perfect requirements.
Frequent, practical use of AI in analytics workflows, with concrete examples of how it has changed pipeline scaffolding, coding and code review, data validation, or documentation, and an understanding of where human verification is essential.

Responsibilities

Own ambiguous CX data problems and design analysis and measurement frameworks.
Introduce policies and practices to monitor and enforce data integrity within the role's domain.
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 shape CX strategy.
Reconcile conflicting and messy data sources to establish reliable sources of truth.
Use AI to accelerate hypothesis generation, code drafting, cross-source reconciliation, validation, and documentation, 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 streamline dashboards for daily operations and self-service insights.
Independently determine analytical research, metrics, and data products to achieve annual objectives, and proactively resolve stakeholder conflicts or misalignment.
Improve data modeling and schema-design practices, expand toolsets such as Databricks where they advance CX outcomes, and improve data quality, rigor, and adoption of data capabilities.
Mentor other data practitioners.
Present complex findings clearly and compellingly to technical and non-technical stakeholders and foster a data-informed mindset.