Senior ML Data Engineer
About The Position
Public Storage is expanding its creative team to enhance its consistent and engaging visual brand presence. We are looking for a Senior ML Data Engineer to architect, build, and maintain batch and streaming data pipelines, design and implement layered data models, and establish best practices for data quality. This role will also involve ML/AI platform engineering, converting prototypes into production-grade systems, and deploying/managing training and inference workflows. Additionally, the role will support real-time, event-driven inference and streaming feature delivery for mission-critical decisions, contribute to internal LLM-based assistants, and implement model monitoring frameworks. The Senior ML Data Engineer will collaborate with data scientists, analysts, and engineers to operationalize models and data products, and partner with various business teams to support experiments and define success metrics. The ideal candidate is passionate about building high-quality systems, hardworking, dependable, eager to learn, comfortable in a fast-paced environment, and open to collaboration.
Requirements
- 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
- 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
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
Responsibilities
- 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
- 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
- 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
Benefits
- Best Career Growth
- Top 5% for Work Culture
- Top 10% for Diversity and Inclusion
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What This Job Offers
Job Type
Full-time
Career Level
Senior
