Data Lead

CSC Generation

. Build and maintain analytical data models across CSC Generation’s portfolio of 13 brands .

Posted 10/22/2026full-timeUnited StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in SQL and Python for data modeling and analytics, with a strong focus on data validation, integration, and the use of AI tools to enhance analytical workflows. Proficient in building semantic layers and BI data models to support business decision-making across multiple brands.

Highest-signal resume keywords

SQL ProgrammingPython ProgrammingData ModelingBusiness Intelligence ToolsData Validation

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

SQLPythonData ModelingETL/ELT PipelinesData WarehousingAI ToolsKPI DefinitionStatistical AnalysisA/B TestingData Governance

Soft Skills

CollaborationCritical ThinkingExpectation Management

Tools & Technologies

LookerTableauPower BIBigQuerySnowflakeRedshiftAirflowNetSuiteGoogle Analytics

Industry Keywords

E-CommerceRetail AnalyticsMarketing Analytics

Tech Stack

Tools & technologies

AirflowAmazon RedshiftBigQueryCloudERPETLPythonSQLTableau

About the role

Key responsibilities & impact

Build and maintain analytical data models across CSC Generation’s portfolio of 13 brands
Own the analytical layer between raw data and business decisions
Define metrics, build semantic layers, and create validated answers to stakeholder questions
Write, review, and debug SQL and Python code, including AI-generated code
Design and maintain semantic layers and reusable BI data models in Looker, Tableau, Power BI, or similar tools
Integrate and reconcile data across ERP, web analytics, and other systems
Align metric definitions and master data such as customers and products
Validate data rigorously and reconcile discrepancies across data sources
Scope technical feasibility, effort, and trade-offs for new data requests
Define KPIs and translate business questions into actionable analytical recommendations
Use AI tools such as Claude Code and Codex to accelerate analysis, coding, and documentation
Critically review AI-generated outputs
Partner with data engineering, business stakeholders, and external vendors
Maintain data quality across the portfolio
Deliver reconciled cross-brand metrics, reusable BI-layer data models, and faster analytics workflows through AI tooling
Report to the Director of Finance and Business Intelligence

Requirements

What you’ll need

Strong SQL and Python skills, including writing, reviewing, and debugging AI-generated code
Experience with cloud data warehouses such as BigQuery, Snowflake, or Redshift
Experience with data modeling
Familiarity with ERP and web analytics data sources such as NetSuite and Google Analytics
Understanding of ETL/ELT pipelines, data warehousing, and orchestration tools such as Airflow
Experience integrating and reconciling data across multiple systems or business units
Experience aligning metric definitions and master data
Rigorous data validation and governance skills
Proficiency with at least one modern BI tool such as Looker, Tableau, or Power BI
Experience building semantic layers or reusable data models
Fluency with AI coding and analysis tools and ability to critically validate outputs
Experience defining KPIs and translating business questions into analytical approaches
Ability to partner with data engineering, business stakeholders, and external vendors
Ability to set realistic expectations on delivery
Solid foundation in statistics and experimentation, such as A/B testing and hypothesis testing (preferred)
Experience building AI-assisted workflows or enabling analytics teams to adopt AI tools (preferred)
Experience in e-commerce, retail, or marketing analytics (preferred)
Valid LinkedIn profile URL or credible explanation and alternative professional-background evidence
Employment is contingent on a background check, including verification of prior employment dates

Benefits

Comp & perks

Paid time off policies
401(k) match
Medical, dental, and vision insurance
Variety of supplemental insurance policies
Employee discounts across the portfolio of brands
AI-first skill building and professional development through modern AI tooling
Cross-functional impact across marketing, finance, operations, and merchandising
End-to-end ownership of work from raw data to trusted business metrics