> Markdown version of [/jobs/ext/1887973-data-engineer-ii-ai-native](https://www.wearedevelopers.com/jobs/ext/1887973-data-engineer-ii-ai-native). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer II, AI Native - **Company:** Life360 - **Location:** Ventura County, CA, United States (Remote available) - **Experience:** Expert - **Salary:** $47,840.0 - $64,480.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Batch Processing, Big Data, Code Generation, Information Systems, Continuous Integration, Information Engineering, Distributed Data Store, Github, Global Positioning Systems (GPS), Python (Programming Language), SQL Databases, Data Streaming, Large Language Models, Apache Spark, Information Technology, Presto, Terraform, Databricks - **Published:** August 1, 2026 - **Apply:** https://job-boards.greenhouse.io/life360/jobs/8659252002 ## About the Role 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., * 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. 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. Nice to Have * Experience working with an experimentation framework. * Experience designing and maintaining real-time streaming architectures. ## Description Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value., * 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Hacking AI at the Edge of the Indian Ocean](https://www.wearedevelopers.com/videos/100177-hacking-ai-at-the-edge-of-the-indian-ocean) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Bringing AI Model Testing and Prompt Management to Your Codebase with GitHub Models](https://www.wearedevelopers.com/videos/1536-bringing-ai-model-testing-and-prompt-management-to-your-codebase-with-github-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)