The Opportunity

SharkNinja is seeking a Senior Manager, CX Analytics to lead our CX Data capability and build scalable, AI-enabled analytics that power operational excellence across our global Consumer Experience organization.

This role sits at the intersection of Contact Center Operations, Voice of Consumer, Quality, and Enterprise Data Engineering. You will combine deep CX domain expertise with strong analytics engineering capability to engineer trusted CX datasets, standardize global CX metrics, enable structured business self-service, and embed AI to accelerate insight generation—in close partnership with our Global Enterprise Data team.

This is not a pure dashboard development role. It is a CX-native analytics leadership role focused on building durable, scalable data products that improve speed, clarity, and operational decision-making.

What You’ll Own

1. CX Analytics Strategy & Metric Standardization

You will define and standardize how CX performance is measured globally across:

• Contact center performance (e.g., AHT, occupancy, forecasting inputs)
• Quality assurance frameworks and compliance measurement
• Voice of the Consumer analytics
• Contact drivers, defect trends, and root cause analysis
• Operational backlog and order-related contact dynamics
• Cost-to-serve analysis

You must deeply understand how CX metrics are calculated and how operational levers influence outcomes.

1. CX Data Engineering (SQL, Snowflake, dbt)

You will design and implement CX domain models and transformations within the enterprise Snowflake and dbt frameworks.

Responsibilities include:

• Writing advanced SQL (complex joins, window functions, performance optimization)
• Building and maintaining dbt models aligned to enterprise frameworks
• Standardizing metric definitions, documentation, and lineage
• Refactoring legacy reporting logic into scalable, governed data assets
• Ensuring testing, performance reliability, and production-quality code standards

You will operate within enterprise governance standards while owning CX-specific transformation logic.

1. Enabling Self-Service Analytics (Critical to Success)

A key success metric is reducing analyst dependency and enabling structured business self-service. You will:

• Design curated CX data marts optimized for operational leaders
• Build governed semantic layers to prevent metric misinterpretation
• Enable leaders to independently answer first-order questions
• Reduce ad hoc reporting demand through reusable datasets and clear definitions
• Create intuitive analytical pathways rather than complex drill-through journeys

The goal is faster decisions without compromising data integrity.

1. AI-Enabled Insight Acceleration

This role is expected to embed AI into the CX analytics lifecycle to increase speed and depth of insight. You will:

• Use AI-assisted SQL and model development to accelerate delivery
• Apply anomaly detection techniques to CX KPIs and operational metrics
• Automate contact driver clustering and transcript theme extraction
• Leverage NLP for Voice of Consumer categorization and insight discovery
• Deploy LLM-powered summarization to streamline executive reporting, i.e. Snowflake Intelligence
• Evaluate Snowflake-native AI capabilities (e.g., Cortex/COCO) for CX use cases

AI must drive measurable gains in time-to-insight, foresight, and analyst productivity.

 

 

 

Clear Division of Responsibilities

To ensure strong partnership and avoid duplication, responsibilities are clearly defined between the CX Data Team and the Global Enterprise Data Team:

CX Data Team (this role)	Global Enterprise Data Team
CX domain expertise and business requirements definition	Platform architecture, governance, and compliance standards
Writing CX-specific SQL and dbt models within the enterprise framework	Snowflake environment management and engineering best practices
Curating, interpreting, and communicating CX datasets	dbt framework standards, patterns, and code review
Translating stakeholder questions into structured data requirements	Data ingestion pipelines and source system integrations
Design CX data products that power dashboards and self-service analytics across the CX organization	Enterprise tooling (Cortex/COCO, AI-enabled development frameworks)
Validating outputs reflect true CX operational reality	Cross-functional data standards ensuring CX data aligns with Finance, Marketing, Operations

This role operates in close partnership with Enterprise Data—contributing domain logic within established engineering guardrails.

 

What We’re Looking For

Required

• 8–12+ years in Analytics, with strong CX/Contact Center focus
• Deep experience in Contact Center operations, Quality, and Voice of Consumer
• Advanced SQL expertise
• Strong Snowflake experience
• Hands-on dbt model development
• Experience operating within a centralised enterprise data platform where domain teams contribute models within shared governance frameworks.
• Experience building governed data marts or semantic layers
• Experience enabling structured business self-service analytics
• Exposure to AI/ML techniques in analytics workflows

Not a Fit If

• Your experience centers primarily on dashboard configuration
• You have not built transformation logic or data models
• You lack experience in Snowflake or dbt
• You have limited exposure to CX operational metrics

What Success Looks Like

• Globally standardized, trusted CX metrics
• Performant Snowflake datasets aligned to enterprise standards
• dbt pipelines documented and tested
• AI embedded into CX insight workflows
• Increased business-led data exploration
• Reduced manual reporting and faster operational decision-making

Who You Are

• A CX-native analytics leader who understands contact center operations, VOC, and quality measurement
• This is a hands-on technical leadership role. You will actively design and build SQL/dbt models while setting standards for CX analytics engineering
• Curious, structured, and detail-oriented—able to challenge assumptions and clarify requirements
• Energized by building scalable systems and enabling others through self-service analytics