Senior AI​/ML Data Engineer

Since opening our first self-storage facility in 1972,
Public Storage
has grown to become the
largest owner and operator of self-storage facilities
in the world. With
thousands of locations
across the U.S. and Europe, and more than 170 million net rentable square feet of real estate, we're also one of the largest landlords.

We've been recognized asA Great Place to Work
by the Great Place to Work Institute. And, our employees have also voted us as having
Best Career Growth
, ranked us in the
Top 5% for Work Culture
, and in the
Top 10% for Diversity and Inclusion
.

We're a member of theS&P 500
and
FT Global 500
. Our common and preferred stocks trade on the New York Stock Exchange.

Public Storage is the nation’s leading self-storage provider, recognized for its iconic orange doors and commitment to delivering simple, reliable solutions to millions of customers across the country. We are expanding our creative team to enhance our consistent and engaging visual brand presence.

What You’ll Do

• Architect, build, and maintain batch and streaming data pipelines using Big Query, dbt, Airflow/Cloud Composer, and Pub/Sub
• Design and implement layered data models, semantic layers, and modular pipelines that scale as business needs evolve
• Establish and enforce best practices for data quality, observability, lineage, and schema governance
• Optimize Big Query for performance and cost efficiency, including partitioning, clustering, and workload-aware modeling
• Work with both structured data and unstructured data such as web logs, call center transcripts, images, and video when required by the use case
• Leverage BQML and related data science capabilities for use cases such as anomaly detection, classification, and operational decision support
• Deliver reliable, scalable, and high-performing pipelines that enable downstream ML, analytics, and operational applications
• Convert prototype notebooks and models into production-grade, versioned, testable Python packages
• Deploy and manage training and inference workflows on GCP using Cloud Run, GKE, and Vertex AI
• Implement CI/CD, model versioning, rollback strategies, and operational guardrails for ML systems
• Evaluate emerging GCP and third-party products; build shared libraries, templates, and internal tooling that accelerate delivery across teams
• Enable ML teams to ship faster with fewer operational failure points
• Support real-time, event-driven inference and streaming feature delivery for mission-critical decisions, including recommendation systems, dynamic experimentation, and agentic AI use cases
• Contribute to internal LLM-based assistants, retrieval-augmented generation (RAG) systems, and automation agents
• Implement model monitoring, drift detection, alerting, and performance tracking frameworks
• Evaluate and apply graph-based data patterns where they improve recommendations, relationship analysis, knowledge retrieval, or decision intelligence

Cross-Functional Collaboration

• Partner with data scientists, analysts, and engineers to operationalize models, semantic layers, and data products into maintainable production systems
• Collaborate with pricing, digital product, analytics, and business teams to stage rollouts, support experiments, and define success metrics
• Participate in architecture reviews, mentor engineers, and communicate technical trade-offs clearly
• Contribute to an engineering culture grounded in ownership, curiosity, thoughtful debate, and continuous learning

What We’re Looking For

We are looking for someone who is not only technically strong, but also:

• Passionate about building high-quality systems
• Hardworking and dependable, with strong ownership of outcomes
• Eager to learn, experiment, and deepen expertise over time
• Comfortable in a fast-paced environment where priorities can shift quickly
• Open to collaboration, feedback, and healthy technical debate
• Interested in growing both technical depth and leadership capability over the long term

Qualifications

• MS in Computer Science with 4+ years of experience, or BS in Computer Science with 6+ years of experience
• 3+ years of hands‑on experience building data pipelines in a code‑first environment using Python, SQL, and dbt
• 1+ year…