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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Data Engineer, Data Platform Core (AI Native) - **Company:** Life360 - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Batch Processing, Cloud Computing, Code Coverage, Code Generation, Code Review, Data Validation, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Amazon DynamoDB, Information Lifecycle Management, Python (Programming Language), MySQL, SQL Databases, Unstructured Data, Usage Analysis, Chatbots, Data Ingestion, Great Expectations (Foster Youth College-readiness and Support Program), Large Language Models, Claude Code, Apache Spark, Infrastructure Automation Frameworks, Data Analytics, Apache Kafka, Spark Streaming, Data Management, Physical Data Models, Claude, Terraform, Databricks - **Published:** November 8, 2026 - **Apply:** https://www.juju.com/job/28_25303_641dbbbd285bdda3c684f340912c499d ## About the Role * 10+ 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 is 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. ## Description 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 Staff Data Engineer, 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 chatbots that enable non-technical users to interact with data in natural language, automating common data questions and reducing the burden on engineering teams. 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., * 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, 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., Daily use. Our engineers use Claude Code every day to scaffold pipelines, write and review code, generate tests, and draft documentation. Our codebase includes custom Claude skills that enforce team conventions, and we use Databricks Genie to answer common data questions for non-technical users. Judgment and ownership. You own everything you ship, whether you or a model wrote it. You review AI-generated code as if you wrote it, test it as if it were going to production, and refactor it when it doesn't meet our standards. Velocity. We expect AI fluency to show up as output: faster pipeline delivery, broader test coverage, and better documentation, without lowering quality. Team leadership. You share what works, contribute to our shared AI tooling (Claude skills, agent workflows, prompts), and help other engineers get more out of their AI tools. Continuous learning. AI tooling changes quickly. You stay current on new tools and techniques, try them on real work, and bring recommendations to the team.