Director, Data Engineering (AI Native)

Life360

9h • $216,000 - $318,000 • Remote

About The Position

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.

Requirements

• 8–10+ years of experience in data engineering, analytics engineering, or data platform roles at technology companies, with at least 5 years in people management.
• 3+ years managing managers; you know how to lead through others, set org-level direction, and scale teams.
• Define and drive the architectural vision for end-to-end ELT/ETL processes, covering data ingestion, transformation, modeling, and serving at consumer scale.
• Strong technical credibility across data platform and analytics engineering, combined with solid business acumen, consistently tying technical decisions to business impact.
• Strong experience with dbt (data build tool): project structure, testing frameworks, documentation standards, CI/CD for data transformations, and how to scale dbt across multiple teams and domains.
• Production experience with Databricks or equivalent lakehouse platforms (Snowflake, BigQuery) at consumer scale—including pipeline design, orchestration, reliability and compute optimization, cost management, and data lake architecture.
• Demonstrated experience managing multiple teams or workstreams simultaneously (15+ people across distinct functions) at a technology company.
• Strong track record of stakeholder management at the director/VP level—you’re comfortable saying no, explaining tradeoffs, and building trust with non-technical leaders.
• Ability to distill technical complexity into language that non-technical stakeholders can understand and act on. You can run a crisp stakeholder review with a VP of Product as effectively as you can lead an architecture review with your engineers.
• Proven ability to prioritize ruthlessly across competing demands from multiple business units—a skill sharpened by working in fast-paced tech environments.
• Strong business acumen—you understand how product metrics, growth loops, and monetization models connect to data infrastructure decisions.
• AI native mindset: you actively use AI tools in your own work and have a point of view on how AI changes data engineering practices, team productivity, and infrastructure requirements.

Nice To Haves

• Experience at a consumer mobile, subscription-based, or marketplace technology company.
• Hands-on experience with our specific stack: Databricks, Amplitude, Statsig, dbt.
• Experience building or managing ads data infrastructure or ad-tech data pipelines.
• Experience with real-time data streaming and event-driven architectures at scale.
• Track record of implementing AI/ML-powered approaches to data quality, pipeline optimization, or infrastructure automation.
• Experience managing remote-first or distributed engineering teams at tech companies.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.

Responsibilities

• 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.
• 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.
• 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. Be the person who can explain what’s possible, what’s expensive, and what’s worth doing.
• Build strong partnerships with Analytics and Data Science to ensure that platform investments and data models actually serve the analysts, decision scientists, data scientists, and business stakeholders downstream.
• Understand Life360’s business deeply—MAU growth, retention mechanics, subscription economics, ads monetization—and use that understanding to prioritize engineering work that moves the needle.
• Partner with product and business teams to ensure data infrastructure supports experimentation, feature measurement, and decision-making at the speed the business requires.
• Own budget and cost management for data engineering infrastructure, ensuring efficient use of cloud resources and vendor spend.

Benefits

• Competitive pay and benefits.
• Medical, dental, vision, life and disability insurance plans (100% paid for US employees). We offer supplemental plans for medical and dental for Canadian employees.
• 401(k) plan with company matching program in the US and RRSP with DPSP plan for Canadian employees.
• Employee Assistance Program (EAP) for mental wellness.
• Flexible PTO and 12 company wide days off throughout the year.
• Learning & Development programs.
• Equipment, tools, and reimbursement support for a productive remote environment.
• Free Life360 Platinum Membership for your preferred circle.

What This Job Offers

• Job Type: Full-time
• Career Level: Director