Data Lead (Costa Rica)

CSC Generation•San Jose, CA

4d•Hybrid

About The Position

CSC Generation is an AI-native holding company that uses its Genesis operating platform to modernize omnichannel retail across a portfolio of 13 brands. The role is based in Costa Rica and reports to the Director of Finance and Business Intelligence. The role bridges data from the portfolio’s distinct ERP, web analytics, and operational systems, building trusted and reconciled data assets. The Senior Analytics Engineer owns the analytical layer between raw data and business decisions by modeling data, defining metrics, building semantic layers, and working with stakeholders to turn ambiguous questions into clear, validated answers. The first 6–12 months are expected to include delivering trusted reconciled cross-brand metrics, establishing reusable BI-layer data models, and measurably accelerating analytics workflows through AI tooling. This is a lean team role with substantial end-to-end ownership. The work involves SQL and Python development, maintaining analytical models and BI semantic layers, reconciling data and metric definitions across systems and business units, validating results, assessing feasibility and delivery trade-offs for data requests, defining KPIs, using AI tools with critical review, and partnering with data engineering, business stakeholders, and external vendors. The company describes its environment as builder-oriented, fast-moving, cross-functional, and AI-first, with ownership of work from raw data through delivery of trusted business metrics. It offers an attractive benefits package and opportunities for professional growth. The role is described as hybrid in Costa Rica, although the benefits section says work is primarily remote. A mandatory in-person interview at the Escazu office is required before an offer.

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 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 the 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.
Provide a valid LinkedIn profile URL in the application, or provide a credible reason for not having one and alternative evidence of professional background to verify identity.
Attend a mandatory in-person interview with a team member before an offer; candidates unable or unwilling to attend will be removed from consideration.
Employment is contingent on a background check, including verification of prior employment dates.

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 single source of truth 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.
Scope technical feasibility, effort, and trade-offs for new business data requests and set realistic delivery expectations.
Define KPIs and translate business questions into analytical approaches and clear, 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.