Job description

What You’ll Do Data Engineering & Pipeline Development (Primary) — 60% • Architect, build, and maintain batch and streaming data pipelines using BigQuery, 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 BigQuery 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 ML/AI Platform Engineering — 20% • 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 Applied AI & Real-Time Decisioning — 20% • 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 of experience with real-time or event-driven systems such as Pub/Sub, Dataflow, or comparable streaming frameworks • 2+ years of experience owning technical decisions or helping lead engineering direction • 1+ year of hands-on experience with graph databases, including graph data modeling, relationship-centric querying, or graph-based problem solving Preferred Experience • Experience with GCP and/or AWS • Experience with ML platform tooling or monitoring such as MLflow, Evidently, or Vertex AI • Knowledge of semantic search, vector embeddings, LLM orchestration, or RAG workflows • Experience with graph database technologies such as Neo4j, Neptune, or similar platforms • Familiarity with use cases involving knowledge graphs, entity relationships, recommendation systems, fraud patterns, or connected data analysis • Domain experience in pricing, recommendations, forecasting, or large-scale customer analytics • Familiarity with geospatial data or map-based modeling • Some JavaScript experience for lightweight UI or prototyping work Why This Role Will Excite You • You’ll work in a fast-moving environment where decisions are made quickly and your work drives real business outcomes • You’ll own meaningful parts of the platform, not just execute isolated tasks • You’ll build end-to-end solutions—from ingestion and transformation to ML inference and production integration • You’ll have the opportunity to learn quickly, expand your scope, and grow your technical leadership • You’ll join a team that values curiosity, execution, and continuous improvement Workplace • One of our values pillars is to work as One Team and we believe that there is no replacement for in-person collaboration but understand the value of some flexibility. Public Storage teammates are expected to work in the office five days each week with the option to take up to three flexible remote days per month. Public Storage is an equal opportunity employer and embraces diversity. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other protected status. All qualified candidates are encouraged to apply. **Sponsorship for Work Authorization is not available for this posting. Candidates must be authorized to work in the U.S. without restrictions or requiring sponsorship now or in the future. We do not provide training plans or support for F-1 OPT, STEM OPT extensions, or future visa sponsorship.**

Requirements

Must-haves

What You’ll Do
Data Engineering & Pipeline Development (Primary) — 60%
Architect, build, and maintain batch and streaming data pipelines using BigQuery, 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 BigQuery 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
ML/AI Platform Engineering — 20%
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

Nice-to-haves

Experience with GCP and/or AWS
Experience with ML platform tooling or monitoring such as MLflow, Evidently, or Vertex AI
Knowledge of semantic search, vector embeddings, LLM orchestration, or RAG workflows
Experience with graph database technologies such as Neo4j, Neptune, or similar platforms
Familiarity with use cases involving knowledge graphs, entity relationships, recommendation systems, fraud patterns, or connected data analysis
Domain experience in pricing, recommendations, forecasting, or large-scale customer analytics
Familiarity with geospatial data or map-based modeling
Some JavaScript experience for lightweight UI or prototyping work
Why This Role Will Excite You
You’ll work in a fast-moving environment where decisions are made quickly and your work drives real business outcomes
You’ll own meaningful parts of the platform, not just execute isolated tasks
You’ll build end-to-end solutions—from ingestion and transformation to ML inference and production integration

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlaipythongcpci/cdllmsqlawsjavascript

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