Platform Engineer – Enterprise Data & Analytics; GCP​/Kafka

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Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet. A place where tech thinkers and future builders turn bold ideas into breakthrough experiences, we welcome your unique perspective. Help us challenge assumptions, uncover bias, and remove barriers— because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

We are seeking a highly skilled Platform Engineer to design, build, and operate scalable, reliable, and intelligent infrastructure for the Enterprise Data & Analytics Platform. This role combines platform engineering, reliability engineering, and MDM platform integration to support data pipelines, APIs, microservices, and real‑time event‑driven systems.

The Platform Engineer works closely with Data Engineering, MDM teams, SRE, Production Support, and application teams to build resilient, cloud‑native platforms that enable high‑performance analytics, real‑time integrations, and enterprise master data management.

This role emphasizes automation, reliability, API‑first architecture, and GenAI‑assisted engineering to continuously improve platform efficiency and operational excellence.

• Design and build scalable, cloud‑native platform components supporting:

  • Enterprise data pipelines
  • MDM orchestration layers
  • APIs and microservices
  • Real‑time streaming architectures
• Develop microservices and APIs using Java, Spring Boot, and modern frameworks
• Build and maintain event‑driven architectures using Apache Kafka and Google Pub/Sub
• Integrate platform services with MDM systems (e.g., Reltio) and enterprise applications
• Ensure platform scalability, resilience, and performance through modern engineering practices

• Ensure high availability and reliability of platform services and data systems
• Implement monitoring, alerting, and observability using logs, metrics, and tracing
• Participate in incident response, root cause analysis, and reliability improvements
• Define and track SLIs, SLOs, and reliability metrics
• Implement disaster recovery and fault‑tolerant architectures
• Partner with SRE and Production Support teams to improve platform stability

• Build infrastructure‑as‑code using Terraform and Kubernetes
• Automate deployment, scaling, and remediation workflows
• Develop CI/CD pipelines to support rapid and safe releases
• Create reusable platform frameworks and templates to enable self‑service engineering
• Optimize infrastructure cost and performance

• Design and maintain integration layers for MDM platforms (Reltio or equivalent)
• Build APIs and event pipelines for master data synchronization
• Ensure schema consistency and data integrity across systems
• Support real‑time and batch integration patterns

• Support development of real‑time and batch data pipelines
• Optimize data workflows and processing frameworks
• Work with cloud data warehouses and storage systems
• Enable scalable ingestion and transformation architectures

• Use GenAI tools to accelerate development, automation, and troubleshooting
• Apply AI‑assisted log analysis and anomaly detection
• Generate automation scripts, documentation, and test artifacts using AI tools
• Improve operational efficiency through intelligent automation

• Implement monitoring solutions using tools such as Prometheus, Grafana, or equivalent
• Develop dashboards and alerting strategies
• Improve operational runbooks and documentation
• Support follow‑the‑sun operational collaboration

• Implement platform security best practices
• Enforce access controls and compliance standards
• Collaborate with security teams on audits and governance requirements

• Work closely with cross‑functional teams to deliver platform capabilities
• Advocate Dev Ops and platform engineering best practices
• Mentor engineers and contribute to knowledge…