Data Lead (US)

CSC Generation

2d•Hybrid

About The Position

CSC Generation is hiring a Data Lead to bridge data from multiple ERPs, web analytics platforms, and operational systems across its 13-brand portfolio. The role owns the analytical layer between raw data and business decisions by modeling data, defining metrics, building semantic layers, and helping stakeholders turn ambiguous questions into clear, validated answers. The position is described as a Senior Analytics Engineer role within a lean team, with substantial end-to-end ownership and a fast-moving environment. Success in the first 6–12 months means delivering reconciled cross-brand metrics stakeholders trust, establishing reusable data models in the BI layer, and measurably accelerating analytics workflows through AI tooling. The role partners with data engineering, business stakeholders, and external vendors to deliver insights and maintain data quality.

Requirements

Strong SQL and Python skills, including the ability to efficiently write, review, and debug code, including AI-generated code.
Experience with cloud data warehouses such as BigQuery, Snowflake, or Redshift, and with data modeling.
Familiarity with ERP and web analytics data sources such as NetSuite and Google Analytics.
Solid understanding of ETL/ELT pipelines, data warehousing, and orchestration tools such as 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 such as Looker, Tableau, or Power BI, including building semantic layers or reusable data models.
Fluency with AI coding and analysis tools, and judgment to critically validate their outputs.
Strong business acumen and experience defining KPIs and translating business questions into analytical approaches.
Proven ability to partner with data engineering, business stakeholders, and external vendors while setting realistic delivery expectations.

Nice To Haves

Solid foundation in statistics and experimentation, such as A/B testing and 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, such as affiliate or attribution analytics.

Responsibilities

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 such as Looker, Tableau, or Power BI as a trusted source for business metrics.
Integrate and reconcile data across systems and business units, aligning metric definitions and master data such as customers and products.
Validate data rigorously, identify plausible but incorrect results, and reconcile discrepancies across ERP, web analytics, and other sources.
Assess technical feasibility, effort, and trade-offs for new data requests and set realistic delivery expectations.
Define KPIs and translate business questions into analytical approaches and actionable recommendations.
Use AI tools such as Claude Code and Codex to accelerate analysis, coding, and documentation while critically reviewing generated outputs.
Partner with data engineering, business stakeholders, and external vendors to deliver insights and maintain data quality across the portfolio.