Finance Staff Data Engineer, AI Native

Life360 is a company focused on keeping families connected through innovative technology. They are seeking a Staff Data Engineer to enhance their data ingestion and egress frameworks, ensuring high-quality data processing and compliance with financial regulations.

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