Data Lead

CSC Generation is the AI-native holding company re-engineering omnichannel retail. We acquire iconic brands and transform them with Genesis, our operating platform combining a Data Fabric, Automation Engine, proprietary tools, and shared services to modernize operations, elevate customer experience, and expand margins. With $1B+ in revenue across 13 brands, our portfolio includes Sur La Table, Backcountry, One Kings Lane, and others that serve as real-world innovation labs.

Reports to:

Director of Finance and Business Intelligence

Location:

Hybrid – US (Salt Lake City, UT and Austin, TX preferred but flexible)

CSC Generation's portfolio spans 13 brands, each generating data across distinct ERPs, web analytics platforms, and operational systems. This role exists to bridge those sources — building trusted, reconciled data assets that business teams can act on confidently.

As Data Lead, you will own the analytical layer between raw data and business decisions: modeling data, defining metrics, building semantic layers, and partnering with stakeholders to turn ambiguous questions into clear, validated answers. Success in the first 6–12 months means delivering reconciled cross-brand metrics that stakeholders trust, establishing reusable data models in the BI layer, and demonstrating measurable acceleration of analytics workflows through AI tooling.

This is a lean team. You will own a lot, move fast, and make decisions with full end-to-end responsibility.

• Write, review, and debug SQL and Python code — including code generated by AI tools — to build and maintain analytical data models across the portfolio.
• Design and maintain semantic layers and reusable data models in BI tools (e.g., Looker, Tableau, Power BI) that serve as the single source of truth for business metrics.
• Integrate and reconcile data across multiple systems and business units, aligning metric definitions and master data such as customers and products.
• Validate data rigorously, spotting results that look plausible but are wrong, and reconciling discrepancies across ERP, web analytics, and other sources.
• Scope and assess technical feasibility, effort, and trade-offs when business teams bring new data requests, setting realistic expectations on delivery.
• Define KPIs and translate business questions into analytical approaches with clear, actionable recommendations.
• Leverage AI tools (e.g., Claude Code, Codex) to accelerate analysis, coding, and documentation while critically reviewing all AI-generated outputs.
• Partner with data engineering, business stakeholders, and external vendors to deliver insights and maintain data quality across the portfolio.

• Strong SQL and Python skills, with the ability to write, review, and debug code efficiently, including AI-generated code.
• Experience with cloud data warehouses (e.g., Big Query, Snowflake, Redshift) and data modeling.
• Familiarity with ERP and web analytics data sources (e.g., Net Suite, Google Analytics).
• Solid understanding of ETL/ELT pipelines, data warehousing, and orchestration tools (e.g., Airflow).
• Experience integrating and reconciling data across multiple systems or business units, including aligning metric definitions and master data.
• Rigorous approach to data validation and governance with a strong instinct for identifying inaccurate results.
• Proficiency with at least one modern BI tool (e.g., Looker, Tableau, Power BI), including building semantic layers or reusable data models.
• Fluency with AI coding and analysis tools, with the judgment to critically validate their outputs.
• Strong business acumen with experience defining KPIs and translating business questions into analytical approaches.
• Proven ability to partner with data engineering,…