Data Lead (Costa Rica)

CSC Generation Enterprise (CSC) is an AI-enabled multi-brand platform that acquires and transforms legacy businesses into profitable, digital-first, consumer-centric businesses. CSC's platform is purpose built around its core M&A strategy and drives alpha by encoding revenue growth, unit margin management, and other expert-level retail practices into automation and AI. CSC owns and operates more than 10 companies, including Sur La Table, Backcountry, and One Kings Lane. Together, our companies generate $1B+ in revenue and we employ over 2000 people across North America. We are backed by world-class investors and continue to pursue platform and add-on acquisitions at pace.

We are looking for a versatile data professional to help bridge business teams, analytics, and engineering. This role goes beyond routine reporting: you’ll gather requirements from stakeholders, collaborate with data engineers and external partners, improve the structure of our BI environment, and support upcoming changes to our data warehouse and reporting tools. The ideal candidate is hands-on with SQL and BI tools, experienced in Looker dashboard development, familiar with GA4, and able to provide both strategic oversight and lightweight technical support when needed.

This position will report into the Director of Finance & Business Intelligence.

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 Senior Analytics Engineer , 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., 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.

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).

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: We offer an attractive benefits package including primarily remote work, private medical and life insurance, additional paid time off, monthly allowances and reimbursements, employee discounts, and opportunities for professional growth.

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 our Escazu office. 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.

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.