Data Platform Architect leading design of data infrastructure for One Security Platform at Johnson Controls. Collaborating with engineering and AI/ML teams to build a robust, scalable, and secure data platform.
Responsibilities
Lead the design and evolution of the data infrastructure for our flagship One Security Platform (OSP).
Define the architecture, technologies, and governance models for data ingestion, storage, analytics, telemetry, and AI model enablement.
Partner with engineering, product, AI/ML, and cloud operations teams to build a robust, scalable, and secure data platform that powers real-time insights, analytics, and intelligent automation.
Design and evolve the end-to-end data platform architecture that supports telemetry, event streams, metadata, logs, system health, analytics, and ML pipelines.
Define data modeling standards, retention policies, partitioning, and lifecycle strategies for structured and unstructured data across the OSP ecosystem.
Lead the selection and integration of data technologies (e.g., Kafka, Redshift, TimeScaleDB, S3, etc.) aligned to performance, cost, and reliability goals.
Ensure compliance with regulatory, privacy, and security standards (e.g., GDPR, SOC2, NDAA, FIPS) for data at rest, in motion, and in use.
Build and maintain telemetry pipelines that feed dashboards, observability tools, and proactive health monitoring for platform services.
Collaborate closely with platform, cloud, product, and QA teams to ensure data availability, correctness, lineage, and operational efficiency.
Provide technical leadership and mentorship to developers, data engineers, and analytics stakeholders.
Maintain a roadmap for continuous improvement of the data platform with evolving business needs and technology trends.
Requirements
10+ years of experience in software/data engineering, with at least 3 years in a data architect or platform architect role.
Minimum Bachelor's degree in a technical, or engineering, related field.
Experience designing cloud-native, multi-tenant data platforms for SaaS applications (preferably on Azure, AWS or GCP).
Deep expertise in event-driven data pipelines, streaming frameworks (Kafka), and big data storage systems.
Strong hands-on experience with data lake/data warehouse technologies, columnar databases, and time-series storage.
Understanding of DevOps practices, CI/CD integration for data services, and infrastructure-as-code tools (Terraform, CloudFormation).
Strong understanding of security, encryption, access control, and compliance for enterprise-grade data environments.
Exceptional communication and documentation skills — able to evangelize architecture and drive alignment across cross-functional teams.
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