FBS Senior Analytics Engineer

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. What to expect on your journey with us: 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

Seeking a highly skilled and motivated data engineer to join Analytics Engineering team within the Service Analytics and AI organization. This role is pivotal in designing, building, and maintaining scalable data pipelines and analytics solutions that empower Advanced Analytics, Business Intelligence, and Data Science initiatives. You will play a crucial role in building a semantic data layer, defining and implementing cutting-edge data products, and delivering innovative AI-driven solutions that fuel business growth and enhance customer experience. Responsibilities Data Pipeline Development: Lead the design, development, and deployment of scalable and robust data pipelines, ensuring seamless data integration and processing across diverse systems. Analytics Engineering Best Practices: Establish and uphold best practices for data engineering, including coding standards, data governance, performance optimization, and automation strategies. Code Quality and Review: Participate in code reviews, provide constructive feedback, and contribute to the team's continuous improvement in coding practices and methodologies. ETL/ELT Development: Design, build, and maintain robust ETL/ELT pipelines, reusable frameworks, and libraries to process and transform data from diverse sources, ensuring accuracy, quality, and consistency. System Monitoring: Proactively monitor and troubleshoot data pipelines, ensuring high availability, reliability, and performance across all data engineering workflows. Automation and CI/CD: Implement CI/CD pipelines to streamline the deployment, testing, and maintenance of analytics engineering processes. Cross-functional Collaboration: Partner with data scientists, engineers, analysts, product managers, and business stakeholders to understand requirements, translate them into actionable technical specifications, and deliver impactful data solutions. Stakeholder Communication: Articulate complex technical concepts to non-technical stakeholders, fostering alignment and ensuring a shared understanding of data initiatives across teams. SQL & Data Engineering: Advanced SQL development, query optimization, complex transformations, and large-scale data processing. Hands-on experience designing and developing enterprise-scale data solutions using Snowflake including performance tuning, data sharing, tasks, streams, and modern platform capabilities. Experience building scalable transformation frameworks, reusable business logic, testing, documentation, and deployment pipelines using dbt. Star schemas, conformed dimensions, historical tracking, KPI frameworks, and analytical data models. Data quality, lineage, metadata management, testing, observability, and model lifecycle management. Experience delivering data products within Agile product teams and modern cloud environments.