Senior Data Engineer II, AI Native

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with...

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basica...

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

Primary responsibilities include, but are not limited to:
Design, build, and maintain scalable, distributed data pipelines and systems — from ingestion through ELT to storage — supporting streaming and batch processing for real-time analytics, ML, and experimentation.
Automate, test, and harden data workflows to ensure reliability at scale.
Architect logical and physical data models that meet evolving business needs.
Build and maintain features for ML models.
Collaborate with product, analytics, and data science teams to turn data into value.

5+ years of experience working with high volume data infrastructure.
Deep expertise with a modern cloud data warehouse or lakehouse platform (Databricks preferred; Snowflake, BigQuery, or similar also considered) on a major cloud provider (AWS, GCP, or Azure).
Proficient in Python and SQL, with the ability to write and optimize complex queries.
Must have hands-on experience with dbt, ownership of dbt models.
Experience with large-scale data processing using Spark and/or Presto/Trino.
Strong grasp of data modeling, partitioning strategies, storage formats, and analytical workload optimization.
Hands-on experience leveraging LLMs for code generation, analysis, and related work — including reviewing AI-generated output with a close eye on quality, standards, and testing, and owning it as your own.
Experience with modern data engineering tooling — orchestration (Airflow, Databricks Workflows), CI/CD (GitHub Actions), and infrastructure-as-code (Terraform, DABs).
BS in Computer Science, Information Systems, Management Information Systems, Statistics, Mathematics, Data Science, Engineering (Computer, Electrical, or Software), or a related quantitative field.
AI-Native Expectations
The team leverages LLMs to support code generation, analysis, and other use cases. Your experience with AI / LLM usage should include managing code generation with a close eye on quality, standards, and testing — owning the outputs as your own. Your work with and ability to leverage these tools will drive your velocity and ability to effectively work within our environment.
Experience working with an experimentation framework.
Experience designing and maintaining real-time streaming architectures.