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 – Canada. Toronto, ON preferred

About The Role 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.

What You'll Do

• 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.

Required Qualifications

• 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., BigQuery, Snowflake, Redshift) and data modeling.
• Familiarity with ERP and web analytics data sources (e.g., NetSuite, 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, business stakeholders, and external vendors, setting realistic expectations on delivery.

Preferred Qualifications

• Solid foundation in statistics and experimentation (e.g., A/B testing, hypothesis testing).
• Experience building AI-assisted workflows or enabling a team to adopt AI tools for analytics.
• Experience in e-commerce, retail, or marketing analytics (e.g., affiliate, attribution).

Why Join The people who do best here are builders. They take ownership, move fast, and want to see the direct impact of their work.

• Cross-Functional Impact: Your work touches every brand in the portfolio — the data models and metrics you build become the foundation for decisions across marketing, finance, operations, and merchandising.
• AI-First Skill Building: You will work with modern AI tooling as a core part of your workflow, not as an experiment, building skills that define the next generation of analytics engineering.
• End-to-End Ownership: You own the full path from raw data to trusted business metric — scoping, modeling, validation, and delivery.
• Competitive Benefits: Paid time off policies, RRSP match, medical, dental, and vision coverage, a variety of supplemental benefit options, and employee discounts across our portfolio of brands.

Interview Process

• Recruiter Screen - A 30-minute conversation with our recruiting team to align on the role, your background, and what you are looking for.
• Hiring Manager Interview - A deeper conversation focused on your analytics engineering experience, approach to data modeling, and how you work with stakeholders.
• Technical / Case Discussion - A working session covering SQL, data modeling, and how you approach data quality and reconciliation challenges.
• Team member / Panel Interview - Meet the team!
• In-Person Interview - We'd like to meet you at one of our hub offices (Salt Lake City, Austin, or Toronto). Details and logistics will be arranged with your recruiter.
• Reference Checks - Conducted in parallel with the final stages where possible.
• Offer - We move quickly for the right candidate.

Interview process is subject to change. Any updates will be communicated promptly and clearly.

Please Note

• Part of our interview process is a mandatory in-person interview with someone on our team prior to an offer. Candidates that are unwilling or unable to meet for an in-person interview will be removed from consideration immediately.
• Due to a high volume of fraudulent applications, you must share a valid LinkedIn profile URL in the application questions below to be considered . If you do not have a LinkedIn profile, you must provide a credible reason in that field and supply alternative evidence of your professional background to verify your identity.