Director, Data Engineering (AI Native)

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 97.8 million monthly active users (MAU), as of March 31, 2026, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com.

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 and Analytics (DnA) organization is the backbone of how Life360 understands its members, measures what matters, and makes better decisions. We own the data platform, analytics engineering, data science, and analytics functions that power insights for every operating group across the company. Our work ensures that every team at Life360 has reliable, well-modeled data and the tools to act on it. We’re a team that values clarity, directness, and building things that work.

About the Job

We’re looking for a Director of Data Engineering to lead the engineering half of our Data and Analytics organization. This is a senior leadership role where you’ll own the data from end to end; from the source to consumption by humans and LLMs. You will manage a strong team of data & analytics engineers and managers and the strategic direction of our data, collaborating closely with leaders on data science, engineering, product analytics, and marketing analytics.

This role requires someone who is technically credible across both data platform engineering and analytics engineering—not just one or the other. You need to be able to evaluate Databricks architecture decisions, understand how data flows through instrumentation SDKs and event collection pipelines, and challenge your managers' technical proposals on pipeline design just as readily as you can assess dbt project structure, data modeling standards, and semantic layer strategy. You won’t be writing code day-to-day, but you’ll need enough depth to set technical direction, spot when something is off, and earn the trust of strong engineers.

Data engineering at Life360 is a strategic partner, not a service org. You’ll be embedded in product and business decisions—shaping roadmaps, pushing back when data needs aren’t accounted for, and designing systems for needs that haven’t been articulated yet.

Your primary impact will come from how you lead people, align teams to business priorities, translate technical complexity into language the business can act on, and represent data engineering at the leadership table.

We’re an AI native company, and we expect this leader to bring that mindset to data engineering. That means actively leveraging AI tools to accelerate development, exploring AI-powered approaches to data quality and pipeline optimization, and building infrastructure that supports ML and AI workloads alongside traditional analytics.

What You’ll Do

Technical Strategy & Execution

• Define and drive the technical roadmap across data platform, analytics engineering, and ads data infrastructure. Set the architectural vision for how data is ingested, transformed, modeled, and served at Life360.
• Own the analytics engineering strategy end-to-end: dbt project structure, data modeling standards (dimensional, OBT, and semantic layer), testing and documentation practices, and the development workflow that analytics engineers use daily.
• Oversee the data platform: Databricks infrastructure, compute optimization, pipeline orchestration, data lake architecture, and the reliability/observability stack that keeps it all running at consumer scale.
• Drive toward a self-serve data experience where analysts and data scientists can answer their own questions without engineering bottlenecks—this is the outcome that ties platform and analytics engineering together.
• Make strategic build vs. buy decisions across the data stack and manage vendor relationships (Snowflake, Databricks, Amplitude, and related tooling).
• Drive data quality, governance, and documentation standards that make data trustworthy and self-service across the company.
• Bring an AI native approach to data engineering: leverage AI tools to accelerate development cycles, evaluate AI-powered data quality and anomaly detection solutions, and ensure our data infrastructure supports ML/AI workloads and experimentation at scale.
• Stay current on emerging technologies in the data and AI space and make pragmatic decisions about adoption—knowing when a new tool solves a real problem vs. when it’s a distraction.

Leadership & People Management

• Lead and develop three engineering managers and their teams, setting clear expectations, running effective org-level planning, and building a high-performance culture.
• Coach and grow your managers as leaders—not just executors. Invest in their ability to hire, develop talent, run retrospectives, and make tradeoffs independently.
• Own workforce planning, headcount allocation, and hiring strategy across all three teams. Run calibrations and drive promotion decisions.
• Build a cohesive engineering org from three distinct functional teams, establishing shared standards while respecting each team’s unique domain expertise.

Stakeholder & Cross-Functional Partnership

• Serve as the primary point of contact for data engineering across Product, Engineering, Finance, Marketing, and executive leadership. Translate business needs into engineering priorities and translate engineering constraints into language the business can act on.
• Manage intake and prioritization across multiple operating groups, balancing competing demands and making clear, defensible tradeoff decisions.
• Represent data engineering in company-level planning, roadmap reviews, and leadership forums.