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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Finance Analytics Engineer, AI Native - **Company:** Life360 - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $103,500.0 - $192,000.0 - **Contract:** Permanent contract - **Skills:** 3d Models, Artificial Intelligence, Airflow, Business Logic, Confluence, Software Documentation, Code Generation, Code Review, Continuous Integration, Information Engineering, Data Warehousing, Cursor (Graphical User Interface Elements), Programming Tools, Github, Python (Programming Language), SQL Databases, Strategies of Testing, Management of Software Versions, AI Infrastructure, Data Ingestion, Large Language Models, Snowflake, Build Management, Virtual Agents, Terraform, Software Version Control, Data Pipelines, Amazon Redshift, Databricks - **Published:** June 9, 2026 - **Apply:** https://www.dice.com/job-detail/daaeb219-0c24-423b-891e-0f1c749a1787 ## About the Role 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., * 4+ years of analytics engineering experience, including deep hands-on work with dbt (dbt core strongly preferred). * 4+ years of SQL experience in an MPP environment (Databricks, Snowflake, Redshift, Trino, or equivalent), with a strong track record of writing performant and maintainable transformations. * 1+ years designing data products for AI consumption: semantic layer work, MetricFlow or Cube, metrics definitions, model contracts, or documentation patterns built with LLM consumers in mind. * Hands-on experience using Claude Code, Cursor, or equivalent AI-enabled development tools as part of your daily workflow. * Strong command of semantic modeling and metrics layer design. * Designing dbt projects at scale: model organization, testing strategy, documentation standards, model versioning, and contracts. * Familiarity with MCP servers and how agents consume warehouse data and metadata, enough to design models and docs that work well for both humans and agents. * Strong written communication: you can write documentation that a stakeholder, a teammate, or an LLM can all use effectively. * Finance or Accounting domain experience. Additional Preferred Experience * Working in a SOX-controlled environment with formal change management. * 1+ years of Python for scripting, API integration, and automation. * 1+ years of Airflow. * Databricks platform specifically, including Unity Catalog. * Familiarity with Terraform, GitHub Actions, or Atlantis for infrastructure and CI/CD. What Sets You Apart * You think about data models as products with consumers, and you treat documentation and semantic clarity as part of the deliverable rather than an afterthought. * You have opinions about what makes a warehouse legible to an agent, and you can defend them. Soft Skills * Comfort with ambiguity and the judgment to make progress when requirements are not fully defined. * Ability to translate technical concepts for Finance & Accounting stakeholders who may not have a technical background. * Self-direction: you can spot what needs doing and move it without constant guidance. * Strong collaboration across Data Engineering, AI Engineering, and Finance teams. ## Description 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) 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 Finance Data Team sits at the intersection of Finance & Accounting teams and Life360's data. We provide the data ingestion / processing / reporting needed by our partner teams in Finance & Accounting to enable their work and ensure SOX compliance with rigor. We push the envelope on how work is done through implementation of AI tools and capabilities to enhance our own pace of development and capabilities that we deliver to our stakeholders. About the Job We are hiring a Senior Finance Analytics Engineer on the Finance Data Team to support data modeling, reporting, and designing data products for an AI-native consumption model. Most analytics engineering teams design for humans writing SQL in a BI tool. We design for both humans and agents. Our models, metrics, and documentation are consumed by Finance & Accounting analysts, by Claude, and by agents working through MCP against our Databricks environment. Semantic clarity, metadata quality, model contracts, and the documentation flywheel across dbt, Databricks, Confluence, and the semantic layer all matter more in this environment than they do in most. Your work will cover: dbt model design, semantic layer and metrics definitions, documentation, testing, and contributing to our AI native development capabilities. This is a senior individual contributor role. You will not be expected to build AI infrastructure or MCP servers, but you should understand how agents consume the data and documentation you produce, and design accordingly. This role reports to the Senior Manager of Finance Analytics Engineering, supporting the Finance Data Team that owns the Finance Data Warehouse., * Design and build dbt models that serve as the source of truth for Finance & Accounting reporting, planning, and analysis. * Partner directly with stakeholders in Finance, Accounting, Revenue, and FP&A to define metrics, shape requirements, and translate business logic into well-structured models. * Write and optimize complex SQL against our Databricks environment with attention to accuracy. * Uphold and evolve patterns for model design, testing, versioning, and data contracts across the dbt project. Designing for AI Consumption * Design the semantic layer and metrics definitions (MetricFlow, Cube, or equivalent) that both humans and agents query against. * Drive the documentation flywheel across dbt, Databricks, Confluence, and the semantic layer so that models, columns, and metrics are legible to LLMs and analysts alike. * Design model grain, naming, and structure so an agent can find what it needs in the warehouse without a human guide. * Use AI tooling (Claude Code, Cursor, and our internal capabilities) as a daily part of your own development workflow, and feed real signal back to the team on what design choices make agents more or less effective. Operations & Compliance * Build and operate within our SOX-controlled CI/CD environment, with no direct human touches to production. * Maintain documentation and auditability of the data pipelines you own. * Participate in code review and approval workflows for SOX-controlled change management. AI / LLM Usage The Finance Data Team leverages LLM's to support code generation, analysis, and other use cases. * Cursor / Claude Code. * Other tools (Wispr Flow, Claude & Claude Cowork, Gemini / Codex). ## 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) - [TresJS a new declarative ThreeJS as Vue components](https://www.wearedevelopers.com/videos/543-tresjs-a-new-declarative-threejs-as-vue-components) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [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) - [Cross platform Augmented Reality development with React Native](https://www.wearedevelopers.com/videos/160-cross-platform-augmented-reality-development-with-react-native) ## Related Articles - [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) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)