Senior Data Engineer

Bamboo Insurance is looking for a highly versatile and hands-on Senior Data Engineer to help design, build, and operate a modern cloud data platform. The role focuses on evolving the data platform to support AI-assisted data operations and ensuring reliable data flows across the organization.

Responsibilities:

• Integrate data from multiple external systems and ensure it flows reliably into the organization’s data platform
• Build scalable pipelines that can accommodate evolving schemas, new data sources, and changing business needs
• Ensure data accuracy and completeness across ingestion, transformation, and warehouse layers
• Maintain a high-performing Snowflake environment that supports analytics and reporting workloads at scale
• Improve the reliability, observability, and maintainability of data pipelines and data infrastructure
• Continuously evolve the data platform so new integrations and data products can be delivered quickly and safely
• Enable AI-assisted exploration of enterprise data by building structured metadata, lineage, and semantic layers that allow AI systems to reason about datasets and pipeline behavior
• Implement modern data architecture patterns such as layered data models (e.g., raw, standardized, curated) to support scalable analytics
• Build structured data models and metadata layers that provide clear context about datasets, schemas, and relationships so both users and AI systems can reliably understand and query data
• Design and maintain Snowflake data models and transformations that support reliable reporting and analytics workloads
• Optimize warehouse performance and manage compute usage to balance performance and cost
• Support backup, recovery, and resiliency strategies across data warehouse and staging environments
• Design and maintain scalable pipelines that ingest data from APIs, files, and external systems into the data platform
• Transform structured and semi-structured data into reliable warehouse-ready schemas
• Build reusable ingestion and transformation patterns that simplify onboarding of new data sources
• Manage and improve the cloud infrastructure that supports the data platform
• Implement disaster recovery practices including automated backups and recovery validation
• Maintain archival processes and support historical data recovery when needed
• Implement monitoring and alerting to ensure data pipelines operate reliably
• Build automated checks to validate data accuracy, completeness, and consistency across systems
• Enable AI-assisted monitoring and troubleshooting by exposing pipeline telemetry, metadata, and operational signals in a structured and accessible way
• Maintain operational documentation, pipeline dependencies, and system runbooks
• Partner with data analysts, engineers, DevOps, and business stakeholders to deliver trusted data systems
• Support AI-assisted data exploration that allows business users and analysts to discover datasets, generate queries, and explore insights through conversational interfaces
• Support security, access control, and governance practices across the data platform
• Manage data movement between environments and support operational workflows for development, testing, and production systems
• Explore and implement emerging AI capabilities that assist with data discovery, pipeline monitoring, and operational troubleshooting using platform metadata and system telemetry

Requirements:

• Strong SQL and Python skills for building and maintaining data pipelines and transformations
• Experience designing and operating cloud-based data platforms
• Strong experience working with modern data warehouses such as Snowflake
• Experience integrating external systems and APIs into data pipelines
• Familiarity with modern data ingestion, transformation, and orchestration approaches
• Experience working with cloud infrastructure supporting data systems
• Understanding of database performance, query optimization, and system tuning
• Experience implementing monitoring, reliability, and operational practices for data pipelines
• Ability to design and maintain automated data validation and quality checks
• Familiarity with building contextual data layers (metadata, lineage, schemas, documentation, operational logs) that enable AI systems to reason about data platforms
• Experience in applying AI techniques to improve data discovery, pipeline monitoring, and analytics workflows
• Experience enabling conversational or AI-assisted interfaces that help users explore datasets or generate analytical queries
• Strong documentation, collaboration, and problem-solving skills
• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field
• 5+ years of experience in modern data engineering skills, data infrastructure, or similar roles
• Experience delivering and maintaining data pipelines in cloud-based production environments
• Track record of managing Snowflake performance and AWS-hosted systems at scale
• Exposure to building AI-enabled data platforms, including semantic data layers, metadata services, or conversational analytics interfaces
• Demonstrated ability to design and operate reliable data systems that support analytics and reporting
• Master's degree in Computer Science, Information Systems, Engineering, or a related field
• Experience in at least one domain-specific data model such as P&C Insurance, CRM, or Call Center systems
• Familiarity with real-time or event-driven data architectures
• Experience with data governance, access control and compliance frameworks
• Exposure to infrastructure-as-code and CI/CD practices for data platforms
• Relevant certifications related to cloud platforms or modern data systems