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Salary: USD 148,000 - 218,500 / annual
Life360 is a category-leading mobile app and IoT platform serving 91.6 million monthly active users across 180+ countries. The company provides location sharing, family safety features, pet GPS tracking, and crash detection with emergency dispatch capabilities. Life360 is a remote-first organization with 500+ employees.
The Analytics Data Engineering team designs, builds, and maintains scalable data infrastructure that empowers Life360 teams to make data-driven decisions. The team transforms raw data into reliable, accessible, and actionable insights while ensuring data quality, compliance, security, and performance.
As a Senior Analytics Engineer, you will be responsible for transforming Life360's massive data volume—60 billion unique location points, 12 billion user actions, and 8 billion miles driven monthly—into trusted, well-modeled datasets that power analytics, reporting, and data science initiatives across the organization.
Key responsibilities include:
- Design and implement robust dimensional and relational data models supporting analytical use cases across Product, Marketing, Operations, and Finance
- Build and maintain scalable dbt transformation pipelines with high data quality, performance, and cost-efficiency
- Own transformation and modeling of curated (Silver/Gold) datasets with clear contracts and traceability
- Partner with data engineering to build and maintain data pipelines and Delta Lake tables within Databricks, including ingestion, transformation, and orchestration
- Collaborate with data analysts, product analytics, data scientists, and business stakeholders to translate requirements into durable data products
- Implement data quality tests, monitoring, SLAs, and alerting for critical analytical datasets
- Leverage AI/LLM tools (Claude, Cursor, CoPilot) to accelerate development while maintaining code quality and standards
You should have a strong foundation in data modeling, SQL, and Python; deep understanding of business metrics; and a passion for making data accessible to stakeholders at all levels. Experience with Databricks, dbt, and AI-assisted development is expected. The role combines analytics engineering with some data engineering responsibilities within a Databricks-based platform.
