Director, Data Engineering – AI Native

🕒 il y a 2 mois

🌐 États-Unis, Canada – Distant

🇺🇸 États-Unis – Télétravail 🇨🇦 Canada – Télétravail

🏄 California – Distant

🏄 California – Remote

💵 $216 000 - $318 000 / an

⏰ Temps Plein

🔴 Expert

🚰 Ingénieur Data

🦅 Parrain de Visa H1B

Cette entreprise a parrainé des visas H1B par le passé.

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Life360

Site WebLinkedInTous les Emplois

201 - 500 employés

Fondée en 2008

👥 B2C

📡 Télécommunications

💰 Post-IPO Equity en 2022-11

B2C • Safety • Telecommunications

Life360 est une application de sécurité familiale de premier plan qui offre une gamme complète de services pour la sécurité de localisation et numérique. Avec Life360, les utilisateurs peuvent partager leur localisation sans effort, suivre leurs appareils et gérer les mesures de sécurité routière, y compris la détection des accidents et l'assistance routière 24/7. L'application inclut des fonctionnalités pour la sécurité numérique, telles que la protection contre le vol d'identité et les alertes SOS, garantissant ainsi la protection et la prévention de chaque membre de la famille. Les forfaits de Life360, qui incluent des options gratuite, Gold et Platinum, sont conçus pour répondre à divers besoins, offrant une tranquillité d'esprit grâce à des outils avancés de sécurité et de coordination. L'application est appréciée par des millions d'utilisateurs et est hautement notée sur les stores pour son efficacité et sa fiabilité à connecter les membres de la famille et assurer leur sécurité.

Description

• 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.

🎯 Exigences

• 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.

🏖️ Avantages

• 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.