Principal Streaming Data Architect

Motorola Solutions•Chicago, IL

1d•$158,800 - $185,600

About The Position

Motorola Solutions creates innovative, mission-critical communication solutions and services that help public safety and commercial customers build safer cities and thriving communities around the world. We look for the most advanced problems solvers - like you - to help us create technologies that rise to the challenges of our customers. To connect firemen to policemen to emergency responders to nurses to specialists to lives saved. To protect communities, grow businesses and make work safer and more efficient. Motorola Solutions is looking for self-motivated individuals to help drive the next generation public safety experience, and to work with a team of top experienced professionals to enable mission critical services and solutions.

Requirements

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

Responsibilities

Lead the real-time data streaming and processing architecture for our nationwide sensor detection platform.
Work with engineering, product, compliance, and infrastructure teams in developing 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 the optimal stream processing frameworks to process high-velocity data efficiently and securely.
Use advanced distributed systems engineering to 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 to ensure consistency and high data quality across all sensor telemetry pipelines.
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 (e.g., anomaly detection, predictive tracking) 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.