Finance Staff Data Engineer, AI Native

Life360 is a company dedicated to keeping families connected through innovative technology. They are seeking a Staff Data Engineer to enhance their Finance Data Team by architecting scalable data ingestion and egress frameworks, improving developer velocity, and ensuring data quality and security. The ideal candidate will have extensive experience in building data platforms and leveraging AI tools to drive development.

Responsibilities:

• Architect and evolve scalable data ingestion and egress frameworks and pipelines that are well tested and offer strong data quality monitoring
• Architect and evolve our CI/CD processes - enhancing the testing environment and observability (such as building LLM-driven reviews with context awareness through data diffing, lineage analysis / downstream impact analysis, and general context)
• Architect delivery architecture of data assets to external team partners to reduce manual operational overhead associated with month end close
• Enhance our Claude Code / LLM development support capabilities - creating tools / skills / agents that give our LLMs more context and help us continually improve their abilities to debug, create code, and maintain systems
• Enhance our security posture in our AWS / Databricks environment
• Design and implement distributed data processing systems using Spark and Databricks on AWS
• Establish clear ingestion and integration boundaries that eliminate single points of failure
• Proactively surface risks, dependencies, and tradeoffs before they impact delivery
• Produce clear technical artifacts and recommendations for stakeholders and leadership
• Design logical and physical data models balancing flexibility, performance, governance, and scalability
• Partner closely with the Analytics Engineers on the Finance Data Team to support high-quality downstream data modeling & reporting
• Harden pipelines with monitoring, alerting, SLAs, and recovery mechanisms
• Mentor engineers and elevate distributed systems rigor across the team

Requirements:

• 8+ years designing and operating high-volume distributed data systems in production
• Deep expertise with a cloud data platform (Databricks strongly preferred) and AWS from an infrastructure / services architecture, deployment, and ownership perspective
• Strong proficiency in Python, SQL, and Spark for large-scale processing
• Strong proficiency with modern CI/CD practices (creating GitHub Actions, writing Terraform code to manage infrastructure in Databricks / Airflow / AWS / and others)
• Hands-on experience with dbt from an infrastructure / deployment perspective and understanding of how platform decisions impact downstream modeling
• Strong grasp of data modeling, partitioning strategies, storage formats, and analytical workload optimization
• Experience with Airflow and data flow orchestration
• Experience with networking challenges in data ingestion (e.g., VPC peering, firewall traversal, API rate limiting, cross-AWS account access, etc.)
• Able to effectively leverage / oversee LLM-supported code development while maintaining a high quality bar
• Demonstrated experience with AI tools to support / enhance development - Claude Code, Cursor, etc
• Demonstrated ability to independently scope ambiguous problems and drive them to decisive outcomes
• Track record of proactively escalating risks and closing long-running efforts with clear recommendations
• Experience defining ingestion validation standards and implementing data quality controls
• Proven ability to reduce operational fragility and eliminate single points of failure
• Strong systems design skills across distributed and event-based architectures
• Demonstrated technical leadership influencing cross-team architectural decisions
• Excellent communication skills across engineering, analytics, product, and executive stakeholders
• BS in Computer Science, Engineering, Mathematics, or equivalent experience