Director, Data Engineering

About the role

Life360 is seeking a Director of Data Engineering to lead the technical direction and execution for our data platform and analytics engineering functions. This senior leadership position involves owning the data lifecycle from source to consumption, managing a team of engineers and managers, and collaborating across departments to drive data-informed decisions. You will be instrumental in shaping our AI-native data strategy.

Key facts

What you'll do

Define and execute the technical roadmap for data platform and analytics engineering, establishing the architectural vision for data ingestion, transformation, modeling, and delivery.

Oversee the analytics engineering strategy, including dbt project structure, data modeling standards, testing, and development workflows.

Manage the data platform, focusing on Databricks infrastructure, pipeline orchestration, data lake architecture, and reliability at scale.

Champion a self-serve data environment for analysts and data scientists, reducing engineering bottlenecks.

Make strategic decisions on data stack tools and manage vendor relationships.

Implement AI-native approaches in data engineering, utilizing AI tools for development acceleration and exploring AI-powered solutions for data quality and pipeline optimization.

Lead and develop three engineering managers and their respective teams, fostering a high-performance culture and individual growth.

Serve as the primary data engineering liaison for Product, Engineering, Finance, Marketing, and executive leadership, translating business needs into technical priorities.

Manage the intake and prioritization of data engineering requests from various business units, balancing competing demands.

Deeply understand Life360's business, including user growth, retention, and monetization, to align engineering efforts with business objectives.

Manage the data engineering budget and cloud resource utilization for cost efficiency.

Requirements

10+ years of experience in data engineering, analytics engineering, or data platform roles within technology companies, with a minimum of 5 years in people management.

3+ years of experience managing other managers, demonstrating the ability to lead through others and scale teams effectively.

Proven ability to define and drive the architectural vision for end-to-end data processes, from ingestion to serving at scale.

Strong technical understanding of both data platform engineering and analytics engineering, coupled with solid business acumen.

Extensive experience with dbt, including project structure, testing, documentation, and scaling across multiple teams.

Production experience with Databricks or comparable lakehouse platforms (Snowflake, BigQuery) at scale, covering pipeline design, orchestration, optimization, and cost management.

Demonstrated success in managing multiple teams or workstreams (15+ individuals across distinct functions) in a technology setting.

A strong track record of managing stakeholders at the director/VP level, including the ability to communicate trade-offs and build trust.

Ability to translate complex technical concepts into clear, actionable language for non-technical audiences.

Proven capability to prioritize effectively across competing demands from multiple business units in fast-paced environments.

Solid understanding of how product metrics, growth, and monetization models influence data infrastructure decisions.

An AI-native mindset, actively using AI tools and understanding their impact on data engineering practices and infrastructure.

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.

Nice to have

Experience in a consumer mobile, subscription-based, or marketplace technology company.

Hands-on familiarity with Databricks, Amplitude, Statsig, and dbt.

Experience building or managing advertising data infrastructure or ad-tech data pipelines.

Experience with real-time data streaming and event-driven architectures.

Experience implementing AI/ML solutions for data quality, pipeline optimization, or infrastructure automation.

Experience managing remote-first or distributed engineering teams.

Skills & tools

Databricks, dbt, Snowflake, BigQuery, Amplitude, Statsig

Practical notes

For candidates based in the US, the salary range is $216,000 to $318,000 USD. For candidates based in Canada, the salary range is $251,000 to $295,000 CAD. Compensation will vary based on location, knowledge, skills, and experience. The compensation package includes medical, dental, vision, financial, and other benefits, along with equity.