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 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 emergency dispatch. Life360 serves approximately 97.8 million monthly active users (MAU), as of March 31, 2026, across more than 180 countries. 

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 basically family). 

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

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.

Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

The Data & Analytics team is building Life360's next-generation data platform — powering real-time decision-making, experimentation, ML, and large-scale analytics. We operate at significant scale and complexity in a high-ambiguity environment, and we expect engineers to own problems, drive clarity, and raise the bar across the org.

We're looking for a high-intensity engineer who wants to envision, design, and build high-impact data products with cutting-edge tools — all while keeping our members ahead of the metrics. You'll become the go-to person for data products across the business, joining a collaborative team that communicates openly, celebrates wins together, and is committed to helping everyone do their best work.

At Life360, we collect a lot of data: tens of billions of unique location points, tens of billions of user actions, billions of miles driven every single month, and so much more. As a Senior Data Engineer II, you will enhance and maintain our data processing and storage pipelines for a robust and secure data lakehouse. We're looking for a strong engineering background — and just as important, a genuine desire to take ownership and make our data systems world class.

The US-based salary range for this position is $148,000 to $216,500. We take into consideration an individual's background and experience in determining final salary - therefore, base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. The compensation package includes a wide range of medical, dental, vision, financial, and other benefits, as well as equity.

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.
• Hands-on experience with dbt.
• 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.