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.
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.