• Diseñar modelos entidad-relación y definir métricas, relaciones y cardinalidades para construir los maestros corporativos y productos de datos. • Implementar esquemas Star/Snowflake, dimensiones conformadas y Slowly Changing Dimensions (SCD) garantizando lógica de negocio reutilizable. • Construir pipelines de transformación con Dataform o dbt, asegurando trazabilidad, rendimiento y disponibilidad de la data analítica. • Implementar controles automatizados de calidad, mantener la documentación en el catálogo de datos y garantizar el cumplimiento de estándares de gobierno. • Transformar necesidades complejas en productos de datos intuitivos y listos para el consumo de analistas y squads de producto. • Aplicar control de versiones (Git), CI/CD, optimización de consultas en BigQuery y monitoreo continuo bajo metodologías ágiles.

• FBS – Farmer Business Services is part of Farmers operations with the purpose of building a global approach to identifying, recruiting, hiring, and retaining top talent. • By combining international reach with US expertise, we build diverse and high-performing teams that are equipped to thrive in today’s competitive marketplace. • We believe that the foundation of every successful business lies in having the right people with the right skills. That is where we come in—helping Farmers build a winning team that delivers consistent and sustainable results. • Since we don’t have a local legal entity, we’ve partnered with Capgemini, which acts as the Employer of Record. Capgemini is responsible for managing local payroll and benefits. • A solid and innovative company with a strong market presence • A dynamic, diverse, and multicultural work environment • Leaders with deep market knowledge and strategic vision • Continuous learning and development

Role Description As a Senior Analytics Engineer, you will serve as the technical leader responsible for building and maintaining the transformation and semantic layer that powers enterprise-wide analytics at PTR. Leveraging our modern data stack—including Fivetran, Snowflake, dbt, and Sigma—you will tackle complex data modeling challenges, establish scalable data models, and build trusted, high-performance datasets that drive business decision-making. Your primary focus is delivering clean, trusted, and well-governed datasets that enable the BI team to create impactful dashboards and insights across the organization. Responsibilities - Core Modeling & Semantic Layer: Design and build the core dbt models and Sigma datasets/semantic models the business relies on, applying dimensional modeling standards and software engineering best practices (modularity, version control, code review). - Enterprise Shared Dimensions: Own the enterprise shared dimensions — customer hierarchy, GL hierarchy, time dimensions, fiscal calendars, territory mappings, and other conformed dimensions used across Finance, Commercial, and Fleet/Ops. - Performance & Optimization: Tune model and warehouse performance in Snowflake; optimize SQL transformations and manage compute/cost efficiency as data volume grows. - Testing, Quality & Observability: Establish and enforce testing and documentation standards in dbt; partner with the team on data quality and observability practices to ensure trust in all published data assets. - Mentorship & Standards: Mentor Analytics Engineer I / junior engineers, review their code, and set the modeling patterns and conventions others follow (naming, layering, environment strategy). - BI Partnership: Partner directly with the BI analyst team to translate dashboard and reporting requirements into robust dataset designs that enable reliable self-service analytics. - Documentation & Versioning: Maintain clear documentation of models, datasets, and transformation logic using version control and CI/CD practices so BI Analysts can self-serve. - Continuous Improvement: Identify opportunities to automate manual processes, reduce technical debt, and improve the scalability and reliability of the analytics layer. Qualifications - Bachelor’s degree in computer science, Analytics, or a related field (or equivalent work experience). - 5+ years of experience in analytics engineering, data engineering, or ELT/data modeling, including demonstrated senior-level ownership of a transformation layer. - Advanced, production-level expertise with dbt for building, testing, and documenting models. - Strong SQL proficiency for performance-tuned querying, transformation, and optimization. - Deep experience with a modern cloud data warehouse (Snowflake strongly preferred). - Proven expertise in dimensional/data model (star and snowflake schemas, conformed dimension, and SCDs) and semantic-layer design. - Experience with Fivetran (or similar ELT/ingestion tooling) and workflow orchestrations such as Astronomer. - Hands-on experience with version control and CI/CD practices for data workflows. - Demonstrated ability to mentor engineers, lead design/code reviews, and set technical standards. - Strong communication and collaboration skills across technical and non-technical stakeholders. - Ability to develop strong business acumen across multiple business units. Requirements - Hands-on experience with Sigma and semantic modeling. - Experience with data observability tools. - Python for scripting, automation, or light transformation logic. - Understanding of data governance, metadata management, and data privacy/compliance standards. Benefits - Premium healthcare coverage (medical, dental, vision, mental health & virtual healthcare). - 401(k) matching and long-term financial planning support. - Paid time off so you can recharge. - Life, accidental death, and disability coverage. - Ongoing learning and development opportunities. - On-site CrossFit-style gym with a full-time personal trainer. - PTR Swag & Uniform/Boot Allowance. - On-site Micro-Markets stocked with snacks and essentials. - Discounts on phone plans, supplier vehicles, mobile detailing, tools, and equipment. - Performance Incentives (Commissions, Bonuses & Profit Sharing).

• Independently ingest, cleanse, transform, and structure 1st party, 3rd party, and internal datasets to support analytical models and client deliverables. • Design and execute validation procedures to ensure data accuracy, consistency, and completeness while proactively identifying and resolving issues. • Execute and manage analytical workflows including data preparation, trade area setup, profiling, competitive analysis, and output generation with minimal supervision. • Develop and maintain repeatable processes for integrating multiple data sources into unified analytical datasets. • Follow established processes and document steps to ensure repeatability and consistency. • Investigate data anomalies, identify root causes, implement solutions, and escalate complex issues when necessary. • Partner with Data Science, Customer Success, and other stakeholders to support project execution and ensure alignment with client objectives.