Principal Streaming Data Architect

Motorola Solutions•Schaumburg, IL

About The Position

Motorola Solutions is seeking a Principal Streaming Data Architect to lead the real-time data streaming and processing architecture for their nationwide sensor detection platform. This role involves working with engineering, product, compliance, and infrastructure teams to develop a stateful, low-latency stream processing engine capable of handling thousands of messages per second from distributed sensors. These real-time data pipelines are crucial for customers monitoring secure perimeters, tracking asset movements, and ensuring public safety. The architect will assess existing data ingestion and processing solutions (including Azure Event Hubs and Azure Databricks), advise on optimal stream processing frameworks, and design and develop stateful stream processing applications for sub-second deduplication of sensor broadcast messages. Responsibilities include designing a robust schema registry and event-driven data modeling strategy, building the technical data governance foundation within the streaming architecture, and collaborating with Data Science teams to operationalize real-time machine learning models. The role also involves developing processes for fault-tolerant, zero data loss, and end-to-end latency consumption of telemetry, partnering with DevSecOps to ensure compliance with CJIS, FIPS 140-3, and FedRAMP High standards, and mentoring junior and mid-level team members. Additionally, the architect will work with geospatial data engineering and backend teams to design and implement pipelines for in-stream data anonymization and feeding real-time UIs and spatial databases.

Requirements

Bachelor's Degree OR 10+ years experience.
Must be a US Citizen due to the nature of some contracts.

Nice To Haves

Experience with Azure Event Hubs and Azure Databricks.
Experience with advanced distributed systems engineering.
Experience with schema registry and event-driven data modeling.
Experience with technical data governance, data lineage, metadata management, and auditability.
Experience operationalizing and deploying real-time machine learning models.
Experience with fault tolerance, zero data loss, and end-to-end latency in data pipelines.
Experience ensuring compliance with CJIS, FIPS 140-3, and FedRAMP High standards.
Experience mentoring junior and mid-level team members.
Experience with geospatial data engineering and backend teams.
Experience with in-stream data anonymization.

Responsibilities

Lead the real-time data streaming and processing architecture for a nationwide sensor detection platform.
Develop a stateful, low-latency stream processing engine capable of handling thousands of messages per second from distributed sensors.
Assess existing data ingestion and processing solutions (including Azure Event Hubs and Azure Databricks) and advise on optimal stream processing frameworks.
Design and develop stateful stream processing applications that achieve sub-second deduplication of sensor broadcast messages across thousands of sensors.
Design and implement a robust schema registry and event-driven data modeling strategy.
Build the technical data governance foundation within the streaming architecture, ensuring strict data lineage, metadata management, and auditability of data flows.
Collaborate with Data Science teams to operationalize and deploy real-time machine learning models directly into the stream processing pipelines.
Develop processes and patterns to consume telemetry from ingestion layers while ensuring fault tolerance, zero data loss, and end-to-end latency from sensor to UI.
Partner with DevSecOps to ensure data-in-transit and in-memory state comply with strict CJIS, FIPS 140-3, and FedRAMP High standards.
Develop the technical skills of junior and mid-level team members by providing hands-on architecture reviews and mentorship.
Work directly with the geospatial data engineering and backend teams to design, implement, and validate pipelines that anonymize data in-stream and feed both real-time UIs and spatial databases.