Data Platform Engineering Manager

About the job

Job description

Data Platform Engineering Manager Johnson Controls is seeking a Data Platform Engineering Manager to lead the strategy, delivery, and operations of the data platform capabilities that support analytics, reporting, telemetry, and data products across the business. This role partners closely with engineering, cloud, product, security, and analytics teams to build a reliable, scalable, and secure data foundation.

Role Overview In this role, you will guide a team responsible for designing and operating modern data platform services, including event-driven pipelines, storage layers, governance controls, and operational monitoring. You will help shape the platform roadmap, improve engineering practices, and ensure the data environment can support both current needs and future growth.

Key Responsibilities

- Lead the design, delivery, and evolution of the enterprise data platform architecture.

- Manage a team of data platform engineers and coordinate work across engineering, cloud, analytics, and product stakeholders.

- Define standards for data modeling, retention, partitioning, lifecycle management, access control, and data quality.

- Oversee the development and support of event-driven data pipelines using technologies such as Kafka and cloud-native services.

- Guide the use of storage and analytics technologies including data lake, data warehouse, columnar database, and time-series storage patterns.

- Partner with teams using platforms such as AWS, Azure, or GCP to support scalable and secure data solutions.

- Improve observability, reliability, and performance across data services and operational workflows.

- Support CI/CD and Infrastructure as Code practices using tools such as Terraform and CloudFormation.

- Collaborate on telemetry, metadata, logs, and system health data that support dashboards, monitoring, and proactive operations.

- Mentor engineers and strengthen team capability through coaching, code review, design review, and knowledge sharing.

How You Will Do It

- You will own outcomes with accountability, safety, and integrity, making clear decisions and following through on commitments.

- You will stay close to the work, remove obstacles quickly, and keep priorities focused on what delivers the most value for customers and internal teams.

- You will improve every day by using data, candid feedback, and structured problem-solving to simplify processes and strengthen the platform.

- You will win as one team by building trust across engineering, product, cloud, security, and analytics groups and by valuing diverse perspectives.

- You will set clear technical standards and help the team execute them consistently across design, build, and operations.

- You will balance strategy with hands-on leadership, ensuring architecture decisions translate into reliable delivery and measurable results.

Qualifications

- 10+ years of experience in software engineering, data engineering, platform engineering, or a closely related discipline.

- Experience leading engineering teams, with responsibility for delivery, quality, and team development.

- Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.

- Experience designing and operating cloud-native data platforms on AWS, Azure, or GCP.

- Strong knowledge of event-driven data pipelines, streaming frameworks such as Kafka, and enterprise data storage patterns.

- Hands-on understanding of data lake, data warehouse, columnar database, and time-series storage technologies.

- Experience with data governance, security, encryption, access control, and compliance in enterprise environments.

- Familiarity with DevOps practices, CI/CD integration, and Infrastructure as Code tools such as Terraform and CloudFormation.

- Strong communication skills with the ability to align technical teams and business stakeholders.

Nice to Have

- Experience with telemetry, observability, or real-time data environments such as logs, metrics, system health, or event streams.

- Exposure to Prometheus, Grafana, Datadog, or ELK.

- Experience supporting ML or advanced analytics pipelines.

- Background in SaaS or multi-tenant platform design.