Sr. AI/ML Data Engineer
About The Position
Public Storage is the world’s best owner and operator of self-storage facilities, serving millions of customers across 3,000+ locations. Public Storage’s Data and AI organization operates like a high-velocity startup inside the enterprise—modern cloud stack, rapid iteration, small expert teams, and direct impact on revenue-critical decisions every day. Our platform is built on Google Cloud (BigQuery, Vertex AI, Pub/Sub, DataFlow, Cloud Run, GKE/Terraform), dbt cloud, Airflow/Cloud Composer, and modern CI/CD practices. We build solutions that driver significant business impact across both digital and physical. Engineers on our team work end-to-end: designing systems, shipping production workloads, influencing architecture, and shaping how AI is applied at national scale. We build for both short and long-term – we are a dynamic, high-velocity engineering team that moves quickly from idea to production. This is a role for someone who wants to own key parts of the data & ML platform, make immediate impact, and thrive in an environment where requirements evolve, decisions matter, and results are visible. You are a passionate, full-stack data & ML engineer who loves to write code, build systems, fun and spirited debates about the “right” architecture for the specific use case. In addition to tech skills, we believe in teaching the soft leadership skills you need to advance your career over the long term.
Requirements
- MS in CS + 4+ years experience or BS in CS + 6+ years experience.
- 3+ years hands-on building data pipelines in a code-first environment (Python, SQL, dbt)
- At least 1 year experience in real-time or event-driven systems (Pub/Sub, DataFlow batch/streaming frameworks)
- At least 2 years owning technical decisions or leading engineering direction
- SQL & BigQuery (schema design, query performance, modeling)
- dbt (semantic modeling, macros, testing frameworks)
- Airflow/Cloud Composer (DAG patterns, retries, alerting, SLAs)
- GCP fundamentals (IAM, networking, container deployments)
- Python (structure, testing, packaging)
- You can explain why you chose a data model, how you improved it, what it mattered and how you measured it
Nice To Haves
- GCP or AWS cloud experience
- Experience with ML monitoring or platform tooling (MLflow, Evidently, Vertex AI)
- Knowledge of semantic search, vector embeddings, or LLM orchestration (RAG workflows)
- Domain familiarity in pricing, recommendations, forecasting or large-scale customer analytics
- Awareness of geospatial data / map-based modeling (nice to have)
- Some JavaScript experience (for lightweight UI/prototyping)
Responsibilities
- Architect, build and maintain batch and streaming pipelines using BigQuery, dbt, Airflow/Cloud Composer, and Pub/Sub
- Define and implement layered data models, semantic layers, and modular pipelines that scale as use-cases evolve
- Establish and enforce data-quality, observability, lineage, and schema governance practices
- Drive efficient BigQuery design (clustering, partitioning, cost-awareness) for structured tabluar data primarily and unstructured data (web logs, call center transcripts, images/videos etc) when the use case requires it.
- Leverage ML/DS capabilities in BQML for anomaly detection and disposition
- Transform prototype notebooks / models into production-grade, versioned, testable Python packages
- Deploy and manage training and inference workflows on GCP (Cloud Run, GKE, Vertex AI) with CI/CD, version tracking, rollback capabilities
- Evaluate new products from GCP or vendors; build internal toolkits, shared libraries and pipeline templates that accelerate delivery across teams
- Support real-time, event‐driven inference and streaming feature delivery for mission-critical decisions such as but not limited to real time recommendation systems, dynamice A/B testing and agentic AI interfacing
- Contribute to internal LLM-based assistants, retrieval-augmented decision models, and automation agents as the platform evolves
- Implement model monitoring, drift detection, alerting, and performance tracking frameworks
- Partner with data scientists and engineers to operationalize models, semantic layers and pipelines into maintainable production systems
- Work with pricing, digital product, analytics, and business teams to stage rollouts, support experiments and define metric-driven success
- Participate in architecture reviews, mentor engineers, and drive technical trade-offs with clarity
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees
