CX Business Intelligence Analyst

About This RoleAI processing…

Key Responsibilities

• 1
  Lead: Own ambiguous problems, design analysis and measurement frameworks, and introduce policies and practices by which data integrity is monitored and enforced within your domain.
• 2
  Execution: Deliver multiple high-impact projects, balancing trade-offs to maximize business value, and maintain clear expectations of deliverables and timelines.
• 3
  Strategic Partnership: Partner with CX leaders, cross-function data-science teams, tool developers (i.e. Salesforce), and data engineering to align on strategy.
• 4
  Analytical Rigor: Apply advanced statistical methods and AI-driven anomaly detection to proactively uncover opportunities, diagnose performance issues, and shape CX strategy, separating signal from noise. Navigate messy, conflicting data sources to establish reliable sources of truth. Use AI to accelerate hypothesis generation, code drafting, cross-source reconciliation, validation, and build agents to automate repetitive tasks.
• 5
  Develop and scale reporting: define DBT and Snowflake data pipeline requirements for evolving CX data sources, and build and maintain monitoring systems and streamlit dashboards for day-to-day operation and self-serve insights.
• 6
  Independence: Work with minimal guidance to prioritize, create, and deliver data projects, proactively resolving conflicts or misalignment across stakeholders.
• 7
  Scale the Craft: Up-level the team’s best practices in data modeling and schema design, expand our toolsets (e.g., Databricks for AI-augmented modeling and anomaly detection) where they accelerate CX outcomes, drive improvements in data quality, rigor, and adoption of better data capabilities, and mentor other data practitioners.
• 8
  Communication: Present complex findings in a structured, compelling way to technical and non-technical stakeholders, fostering a data-informed mindset across the company.

Requirements

• 3+ years of experience in Data Analytics, Data Engineering, or Data Governance.
• Strong SQL and Python proficiency, capable of querying and analyzing large datasets with ease.
• Strong foundation in data modeling, schema design, and data quality best practices, with functional experience working on cloud platforms like Snowflake.
• Proven experience building and maintaining robust data reporting pipelines and ETL workflows.
• Demonstrated experience designing or operating governance for AI-assisted analytics outputs. At minimum: validation standards for AI-generated code and metric definitions, drift and source-of-truth audits on dashboards that feed operational decisions, and documented rules for when human review is non-negotiable on performance-adjacent data products. Comfort working inside an organization's AI use policy.
• Clear, direct communication style. You can explain a complex data investigation to a non-technical stakeholder in 1-2 sentences before diving into the details.
• Comfortable working independently, managing competing priorities, and driving projects forward without waiting for perfect requirements.
• Frequent, practical use of AI in analytics workflows with concrete examples (not aspirational interest). You can describe specific ways AI has changed how you scaffold pipelines, write and review code, validate data, or generate documentation, and you know where human verification is non-negotiable.

Perks & Benefits