Guidewire is seeking an experienced Data professional to join their Analytics Platform team to build an integrated, scalable, secure, and robust Analytics Platform. This platform will enable both internal teams and customers to leverage data for insights, decision making and innovation, powering Analytics, Data Science, and AI-driven applications across their product portfolio.
Requirements
- 8+ years of experience in Data Engineering or Analytics Engineering, with at least 3+ years of hands-on experience designing and implementing large-scale data and ML pipelines
- Prior experience working with cloud native data solutions on AWS, GCP or Azure
- Proficiency in SQL, and at least one data engineering language (Python/Scala).
- Experience in ML feature engineering and collaboration with ML engineers & Data Scientists
- Strong working experience with modern data engineering stack, including but not limited to: Spark, dbt, Feast, Airflow, MLflow, Containers, Iceberg etc
- Hands-on experience with relational (PostgreSQL) and columnar (Redshift, etc.) databases.
- Experience with source control like Git, Infrastructure as Code (Terraform) and CI/CD for data workflows
Responsibilities
- Architect and develop high-quality data assets for both product and AI/ML use cases.
- Design and implement scalable data pipelines to enable self-service analytics and feature engineering and ML driven insights
- Develop data transformations and establish robust data models, ensuring high data quality, and performance in a fast pace environment
- Implement DataOps best practices, including CI/CD pipelines for analytics, ensuring data reliability through automated testing and documentation
- Contribute to data strategy by aligning technical decisions with long-term business needs
- Ensure compliance with evolving data protection regulations, maintaining data security and privacy.
- Promote trust in data quality and implement tools to meet regulatory and business needs.
Other
- Mentor team members to enhance their skills and improve team performance.
- Present deep dives on both technical and product-related aspects to stakeholders
- Excellent communication skills, able to collaborate with both technical and non-technical teams.
- Strong understanding of data management concepts (modeling, warehousing, governance).
- Solid problem-solving and troubleshooting capabilities.
