Data Engineer/Architect

JLL•Mountain View, CA

1d•Onsite

About The Position

JLL empowers you to shape a brighter way . Our people at JLL and JLL Technologies are shaping the future of real estate for a better world by combining world class services, advisory and technology for our clients. We are committed to hiring the best, most talented people and empowering them to thrive, grow meaningful careers and to find a place where they belong. Whether you’ve got deep experience in commercial real estate, skilled trades or technology, or you’re looking to apply your relevant experience to a new industry, join our team as we help shape a brighter way forward. What this job involves - As the Data Engineer/Architect, you are the principal technical owner of the data infrastructure that powers our innovation in managing our client's critical facilities and labs. You will be responsible for designing, building, and optimizing the data architecture on a leading global cloud service provider . Your primary mission is to consolidate and structure complex operational data (from BMS, equipment logs, work orders, etc.) to enable the development of deep analysis tools . This role is foundational to our efforts in predictive maintenance, proactive intelligence, and automated remediation using ML/AI, all within our client’s leading global cloud service provider environment.

Requirements

5-7+ years of hands-on experience in a data engineering or data architect role.
Expert-level knowledge and proven experience with a leading global cloud service providerr , especially BigQuery , Pub/Sub, Dataflow, and IAM.
Mandatory, demonstrable experience with operationalizing Building Management System (BMS), EPMS, or other critical facilities/OT data for analytics.
Strong proficiency in SQL, Python , and data modeling techniques for data warehousing.
Experience in preparing data for and supporting ML/AI models in a production environment (MLOps experience is a major plus).
Familiarity with BI tools like Looker Studio or Tableau and how they connect to data warehouses.
Strong problem-solving and analytical skills with a focus on continuous improvement and optimization.
Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field.
GCP certification (e.g., Professional Data Engineer, Professional Cloud Architect) is highly desirable.
This position does not provide visa sponsorship. Candidates must be authorized to work in the United States without employer sponsorship.

Responsibilities

Data Architecture & Engineering Design and build a scalable, secure, and reliable data architecture on GCP, specifically tailored for critical facilities and operational data.
Develop and maintain robust ETL/ELT pipelines to ingest, consolidate, and process data from diverse and disconnected sources (e.g., BMS, EPMS, equipment logs, sensor data).
Utilize core GCP services (e.g., BigQuery , Pub/Sub , Dataflow, Cloud Storage) to warehouse and manage large-scale data.
Structure and optimize data models in BigQuery to support high-performance queries, advanced analytics, and BI visualization in tools like Looker Studio .
Implement data governance, quality checks, and monitoring to ensure data integrity and reliability.
Innovation & AI/ML Enablement Partner with the Director of Innovation to translate strategic goals into technical data solutions .
Prepare and productionalize large-scale datasets for use in Machine Learning (ML) and Artificial Intelligence (AI) models on GCP (e.g., Vertex AI).
Engineer the data foundation required to build and deploy models for predictive maintenance , proactive operational intelligence , and automated remediation workflows.
Operationalize critical building/space data , making it accessible and actionable for operational teams and analysts.
Collaboration & Technical Support Act as the lead technical expert on data architecture and engineering for the client account.
Collaborate closely with operations teams, data analysts, and our client’s stakeholders to understand data requirements and deliver solutions.
Troubleshoot and resolve complex data-related issues, ensuring minimal disruption to analytics and operational tools.