Principal Data Engineer

Equinix is a global digital infrastructure company that enables organizations to securely connect their applications, data, networks, partners, and cloud environments.

Our global platform brings together enterprises, cloud service providers, networks, and digital ecosystems, helping businesses improve performance, reduce latency, and operate securely at scale. As cloud and AI adoption continue to accelerate, Equinix provides the interconnected digital infrastructure that supports this transformation.

Our Bengaluru engineering organization works closely with teams across North America, EMEA, and APAC, providing an opportunity to solve complex enterprise technology challenges in a highly collaborative and global environment.

About the Team

You will join Equinix's Enterprise Data and Analytics organization, a centralized engineering team responsible for building, modernizing, and operating the enterprise data platform.

The team brings together data from multiple internal and external systems and makes it securely available for analytics, business intelligence, data science, machine learning, and AI use cases across Equinix.

Our enterprise data platform is primarily built on Google Cloud Platform, with a modern technology ecosystem spanning BigQuery, Dataflow, Apache Beam, Composer, Airflow, Pub/Sub, Kafka, Dataproc, Spark, Cloud Storage, Dataform, dbt, Vertex AI, Python, and Java.

The team is focused on developing scalable data products, modernizing existing platforms, improving reliability and cost efficiency, strengthening data governance, and applying AI and Agentic AI to engineering and intelligent data-consumption workflows.

About the Role

As a Principal Data Engineer, you will serve as a senior individual contributor, technical leader, and trusted advisor across the Enterprise Data and Analytics organization.

You will architect and evolve cloud-native, enterprise-scale data platforms while remaining closely connected to hands-on engineering. You will lead complex initiatives from architecture and technical evaluation through implementation, production deployment, and continuous evolution.

In this role, you will:

• Architect scalable batch, streaming, and event-driven data platforms on GCP
• Lead the end-to-end delivery of complex data engineering initiatives from design through production
• Make architectural decisions that balance scalability, performance, reliability, security, operational excellence, and cloud cost
• Drive the modernization of enterprise data platforms, pipelines, and engineering frameworks
• Establish reference architectures, reusable patterns, engineering standards, and architectural guardrails
• Design distributed and fault-tolerant systems with measurable service-level objectives for data freshness, availability, and reliability
• Establish engineering excellence frameworks covering performance, cost governance, observability, testing, and production support
• Architect enterprise capabilities for data quality, lineage, metadata management, governance, security, and regulatory compliance
• Evaluate and apply LLMs, RAG, Agentic AI, and Model Context Protocol to data engineering and intelligent data-consumption use cases
• Lead technical proof-of-concepts and scale successful solutions across the organization
• Mentor Staff, Senior Staff, and Data Engineers and help raise engineering capability across teams
• Partner with engineering, product, analytics, AI, data science, and business stakeholders
• Present architectural decisions and technical recommendations to senior and executive stakeholders
• Contribute to build-versus-buy decisions, technology evaluations, and strategic platform partnership

About You

• Extensive experience in data engineering, distributed systems, and enterprise data-platform architecture
• Deep hands-on experience with Google Cloud Platform
• Strong expertise in BigQuery, Dataflow, Composer/Airflow, Pub/Sub, Dataproc, Cloud Storage, and Dataform/dbt
• Expert-level programming skills in Python or Java
• Strong experience with Apache Spark, Apache Beam, Kafka, and distributed-computing architectures
• Experience designing high-volume batch, streaming, and event-driven data platforms
• Proven ability to build fault-tolerant, observable, secure, and cost-efficient production systems
• Strong knowledge of data modeling, relational and NoSQL databases, data lakes, data warehouses, lakehouse architecture, and data mesh principles
• Experience with Terraform, CI/CD, infrastructure automation, Docker, Kubernetes, and cloud deployment practices
• Strong understanding of data quality, lineage, metadata management, governance, privacy, security, and regulatory compliance
• Experience with platform modernization, performance engineering, cost optimization, and reliability improvement
• Practical experience integrating AI or LLM capabilities into production data platforms or engineering workflows
• Exposure to RAG, vector databases, Agentic AI, LLM orchestration, Vertex AI, or Model Context Protocol
• Experience mentoring senior engineers and establishing engineering standards and best practices
• Ability to influence multiple teams without relying on formal authority
• Strong stakeholder-management and executive-communication skills
• A demonstrated ability to connect technical architecture decisions to measurable business outcomes