Life360 is a Remote First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US and Canada) regardless of any specified location above.
We are AI Native

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.
About The Team

The Data Platform Core team designs, builds, and maintains scalable data infrastructure that empowers Life360 to make data-driven decisions. We transform raw data into reliable, accessible, and actionable insights, ensuring data quality, compliance, security, cost efficiency, and performance at every step. By leveraging innovative technologies and best practices, we enable Product, Analytics, and Partners to unlock the full potential of data, driving operational excellence and strategic growth.

We are also an AI-native team. We embed AI tooling directly into our engineering workflows, from pipeline scaffolding to documentation generation, so our engineers ship faster and our data products improve continuously. If you believe the future of data engineering is human judgment amplified by AI, you'll fit right in.
About the Job

As a Senior Data Engineer on the Data Engineering Core team, you will be a key driver in scaling Life360's data infrastructure to support a product trusted by millions of families worldwide. One of our major pipeline processes handles over 6 billion events per day and approximately 2 TB of data daily from high-throughput event streams via Kafka and Kinesis. We also run batch ingestion pipelines from MySQL, DynamoDB, and internal and external APIs. You'll own these critical pipelines end to end, from raw event ingestion through transformation to delivering clean, modeled datasets in our Databricks Lakehouse that power product decisions, analytics, and partner integrations. Our infrastructure runs on AWS, leveraging Databricks, Kafka, S3, Kinesis, and MWAA (Managed Workflows for Apache Airflow) for orchestration, along with many other AWS services. Beyond building and maintaining our own pipelines, we partner closely with Analytics Engineering, Data Science, and other teams across the organization, providing data infrastructure support and onboarding new and existing data sources into our bronze layer. We also build Databricks Genie-powered chatbots that enable non-technical users to interact with data using natural language, automating frequently asked data questions and reducing the burden on engineering. This role sits at the intersection of platform reliability and engineering velocity, where your work directly unblocks other data teams, data scientists, analysts, and product teams. You'll also help shape how the team builds with AI, contributing to tooling and workflows that make every engineer more effective. You will architect new systems, tackle ambiguous data problems, and raise the bar for how we deliver data at scale.

For candidates based in the US, the salary range for this position is $103,500 to $192,000 USD. For candidates based out of Canada, the salary range for this position is 149,500 to 179,500 CAD. Note: Please be aware that the job title for positions in Canada will be "Developer" in lieu of "Engineer." 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.
AI / LLM Usage

The Core Data Engineering team leverages AI tools as a core part of how we build, not as an optional add-on. Our engineers use Claude daily to write, review, and ship production data pipelines faster. Primary tools we use: Cursor / Claude Code.

Our codebase includes custom Claude Code slash commands that scaffold new pipelines, generate documentation, and automatically enforce team conventions.
We also use Databricks Genie to automate answers to frequently asked data questions and build chatbots that enable non-technical users to interact with data in natural language.
Engineers are expected to contribute to and improve this shared AI tooling over time.

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. You don't just accept what the model gives you. You review it as you wrote it, test it as if it were going to production, and refactor it when it's not up to standard.
What You'll Do

• Design and manage scalable data platforms powering real-time analytics, batch processing, and exploratory analysis, using AI-assisted development as the default workflow, not an afterthought.
• Own the full data lifecycle: ingestion, ETL, storage, and serving, building and iterating on pipelines with AI pair-programming tools (Claude Code) to accelerate delivery.
• Ingest data from diverse sources via both streaming (Kafka, Kinesis) and batch pipelines, unifying them into a consistent, queryable platform.
• Architect medallion-layer data models (Bronze/Silver/Gold) in Databricks, ensuring business needs are met with clean, well-documented schemas.
• Automate, test, and harden data workflows, writing AI-augmented tests, data quality checks, and CI/CD pipelines that catch issues before production.
• Build and maintain AI-ready tooling: craft prompts, custom slash commands, and agent workflows that let the entire team scaffold pipelines, generate documentation, and validate data quality faster.
• Build and improve Databricks Genie chatbots that allow non-technical users to query data using natural language
• Collaborate with product analytics and data science, applying engineering rigor to messy, unstructured data and transforming it into reliable, production-ready datasets.
• Contribute to infrastructure-as-code (Terraform/Atmos) for provisioning and managing cloud data infrastructure.

What We're Looking For

• 5+ years working with high-volume data infrastructure.
• Core stack: Databricks, AWS (EMR, Kinesis/Kafka, S3), Apache Spark/Spark Streaming, Apache Airflow (MWAA), SQL, Python (Java/Scala a plus).
• AI-native mindset: You already use LLM-based dev tools daily, not as a novelty, but as a force multiplier. You can evaluate when AI-generated code is correct, refactor prompts like you refactor code, and build agentic workflows that compound your team's output.
• Experience with data quality frameworks (Great Expectations, DQX, or similar): validation rules, schema enforcement, automated monitoring.
• Proven ability to architect logical/physical data models, optimize SQL, and tune system performance.
• Familiarity with IaC tools (Terraform) for cloud infrastructure provisioning.
• Strong communicator who works independently and ships with minimal supervision.
• BS in CS, Engineering, Math, or equivalent hands-on experience.

Our Benefits

• Competitive pay and benefits.
• Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer supplemental plans for medical and dental for Canadian employees.
• 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
• Employee Assistance Program (EAP) for mental wellness.
• Flexible PTO and 12 company wide days off throughout the year.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership for your preferred circle.