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Public Storage · 7 hours ago

Sr. AI/ML Data Engineer

Plano, TX

Full-time

Hybrid

Mid, Senior Level

4+ years exp

Public Storage is the largest owner and operator of self-storage facilities in the world, recognized for its excellent workplace culture. They are seeking a passionate Sr. AI/ML Data Engineer to architect and maintain data pipelines, transform models into production-grade packages, and support real-time decision-making processes.

Property ManagementReal EstateReal Estate Investment

No H1B

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

You will be accountable for delivering reliable, performant pipelines that enable downstream ML and analytics

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

You will enable the ML team to ship faster with fewer failure-modes

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

Qualification

BigQueryPythonSQLAirflow/Cloud ComposerGCPDbtPub/SubDataFlowReal-time systemsML monitoringSoft leadership skillsCross-functional collaboration

Required

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

Demonstrated expert proficiency in: 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