Engage, Innovate, Grow Efficiently
Data Engineer
Location
United States
Posted
42 days ago
Salary
$124K - $186K / year
Seniority
Mid Level
Job Description
Data Engineer
Guidewire Software
Role Description You will join Guidewire’s Product organization, working at the intersection of data, AI, and cloud infrastructure to power our industry-leading P&C insurance platform. Our team focuses on building secure, scalable, and governed data capabilities that enable AI-infused products and analytics for customers worldwide. We collaborate across Product Development, Security, and Finance to ensure our data platform is robust, reliable, and ready for rapid innovation. In this role, you will design and automate modern data infrastructure on AWS to support Guidewire’s AI-first Product strategy, including Agentic AI Platform initiatives and next-generation analytics experiences. You will lead DataOps practices—CI/CD, observability, and governance—for critical pipelines and data products that power our Claims, Underwriting, and pricing solutions globally. If you are excited by GenAI, large-scale data systems, and building the backbone that helps insurers operate more efficiently, this role puts you at the center of Guidewire’s mission to transform how the world’s P&C insurers do business. Qualifications - Demonstrated ability to embrace AI and apply it to your current role as well as data-driven insights to drive innovation, productivity, and continuous improvement. - 5+ years of experience in Data Engineering, Data Operations, or Platform Engineering building and operating cloud data infrastructure. - Deep proficiency with AWS (e.g., S3, EMR, Glue, Lambda, Redshift) and infrastructure-as-code (Terraform strongly preferred; CDK a plus), including designing secure, resilient architectures. - Strong experience with dbt in production (modeling, testing, documentation, deployment) and modern table formats such as Apache Iceberg for large-scale analytics. - Advanced SQL skills (performance tuning, complex joins and window functions) and solid Python experience for automation, orchestration, and data engineering tasks. - Hands-on experience with Apache Spark for large-scale batch or streaming workloads, ideally on AWS EMR or Glue. - Proven track record of building or maintaining CI/CD pipelines (Git-based workflows, automated testing, deployment, and monitoring) for data and analytics workloads. - Strong systems thinking and data modeling skills (e.g., Kimball, Data Vault) and familiarity with integrating RDBMS data via CDC patterns. - Clear, collaborative communication style with the ability to work across product, security, and business stakeholders in a distributed environment. Requirements - Experience leveraging Claude Code, LLM-based agents, or other agentic AI tools to automate infrastructure provisioning, refactoring, or documentation workflows. - Background working with MLOps platforms such as SageMaker, especially around feature stores and data contracts between data engineering and ML teams. - Experience in a regulated environment (Finance, Insurance, or similar) and familiarity with P&C insurance data concepts (policies, claims, billing, rating, underwriting). - Exposure to Redshift performance tuning, workload management, and cost optimization in enterprise settings. - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. Your Impact We believe in clarity and setting you up for success. In your first six months, you will take ownership of key AWS-based data environments, stabilize and enhance existing dbt/Glue/Spark pipelines, and introduce AI-augmented workflows that reduce operational toil and improve developer productivity. You will help Product Strategy and PDO deliver reliable, trusted data for AI-powered products like the Agentic AI Platform and Claim Summary, while aligning with security and governance priorities for a global P&C customer base. Over time, your work will shape how Guidewire’s customers access, trust, and use data—directly influencing product quality, operational excellence, and our mission to transform how insurers serve their policyholders worldwide. Benefits - Flexible work environment - Health and Wellness benefits - Paid time off programs including volunteer time off - Market-competitive pay and incentive programs - Continual development and internal career growth opportunities
Related Guides
Related Categories
Related Job Pages
More Data Engineer Jobs
• Design, develop, and deploy large scale data processing pipelines, both batch and streaming, using technologies such as Dataflow, Apache Beam, Spark, Akka, Pub/Sub. • Expertise with multiple data storage technologies such as Bigtable/HBase, BigQuery, Spanner, CloudSQL/Postgres. • Work with stakeholders to understand business problems, develop use-cases, and translate them into pragmatic and effective technical solutions. • Design and develop appropriate schema for data based on understanding of the domain problem. • Manage data lineage and ensure data security with appropriate tools and methodologies. • Collaborate with data scientists, architects, and other stakeholders to ensure alignment between technical and business strategy. • Continuously monitor, refine and report on the performance of data management systems. • Mentor junior data engineers, reviewing their outputs and directing their professional development.
• Be a data champion and seek to empower others to leverage the data to its full potential • Architect and own our data platform strategy, with a deep understanding of our complex data ecosystem • Lead the design and implementation of scalable data infrastructure that powers both internal and external products • Partner with Product to translate business requirements for data across the company into a technical roadmap and architecture for the platform • Create and maintain data engineering best practices and mentor teams in building robust, observable pipelines • Assemble large, complex data sets that meet functional / non-functional business requirements • Build the infrastructure required for optimal extraction, transformation, and loading of data from various data sources. • Work with Executive, Clinical, Data, and Design teams, to assist with data-related technical issues and support their data infrastructure needs • Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader
• Integración y modelamiento de datos • Diseñar e implementar pipelines de ingesta y transformación de nuevas fuentes de datos (batch y/o near real-time). • Integrar fuentes internas y externas al modelo de Customer 360, asegurando consistencia con los contextos definidos. • Modelar y estructurar datos para su uso en APIs y analytics . • Construcción y mantenimiento de subproductos analíticos basados en contextos (ej: segmentaciones, features, modelos). • Habilitar datasets listos para consumo por equipos de negocio, analytics y data science. • Colaborar en la definición de nuevos casos de uso basados en datos de cliente. • Implementar controles de calidad, validaciones y monitoreo de pipelines. • Asegurar trazabilidad, documentación y buenas prácticas de data governance. • Optimizar pipelines para performance, costo y escalabilidad. • Trabajar de forma cercana con equipos de negocio, analytics, backend y producto. • Participar en el diseño de nuevos contextos y definición de requerimientos de datos. • Contribuir a la mejora continua de la arquitectura y estándares del equipo.
• Collaborate with a global team of data engineers, fostering coordination across time zones and driving operational excellence. • Architect and oversee large-scale data ingestion and transformation pipelines using Airflow, DBT, Python, Kafka streaming, and Snowflake. • Design and implement real-time and batch ingestion frameworks integrating data from various business systems and APIs. • Manage data integration and transformation from enterprise systems such as Salesforce (SFDC), NetSuite, Workday, and other core business applications. • Drive data quality, observability and reliability across pipelines through monitoring, alerting, and proactive issue resolution. • Collaborate with data science and product teams to support AI assistance bot integration, AI/ML workflows, and product telemetry analytics. • Ensure engineering excellence by aligning data practices with DevOps principles, CI/CD automation, and robust data governance standards. • Oversee multiple projects simultaneously, managing priorities, and delivery timelines in a dynamic, high-velocity environment. • Communicate effectively with business and technical stakeholders to align data engineering initiatives with strategic organizational goals.
