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 TeamYou 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 RoleAs 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 GCPLead the end-to-end delivery of complex data engineering initiatives from design through productionMake architectural decisions that balance scalability, performance, reliability, security, operational excellence, and cloud costDrive the modernization of enterprise data platforms, pipelines, and engineering frameworksEstablish reference architectures, reusable patterns, engineering standards, and architectural guardrailsDesign distributed and fault-tolerant systems with measurable service-level objectives for data freshness, availability, and reliabilityEstablish engineering excellence frameworks covering performance, cost governance, observability, testing, and production supportArchitect enterprise capabilities for data quality, lineage, metadata management, governance, security, and regulatory complianceEvaluate and apply LLMs, RAG, Agentic AI, and Model Context Protocol to data engineering and intelligent data-consumption use casesLead technical proof-of-concepts and scale successful solutions across the organizationMentor Staff, Senior Staff, and Data Engineers and help raise engineering capability across teamsPartner with engineering, product, analytics, AI, data science, and business stakeholdersPresent architectural decisions and technical recommendations to senior and executive stakeholdersContribute to build-versus-buy decisions, technology evaluations, and strategic platform partnership

About YouExtensive experience in data engineering, distributed systems, and enterprise data-platform architectureDeep hands-on experience with Google Cloud PlatformStrong expertise in BigQuery, Dataflow, Composer/Airflow, Pub/Sub, Dataproc, Cloud Storage, and Dataform/dbtExpert-level programming skills in Python or JavaStrong experience with Apache Spark, Apache Beam, Kafka, and distributed-computing architecturesExperience designing high-volume batch, streaming, and event-driven data platformsProven ability to build fault-tolerant, observable, secure, and cost-efficient production systemsStrong knowledge of data modeling, relational and NoSQL databases, data lakes, data warehouses, lakehouse architecture, and data mesh principlesExperience with Terraform, CI/CD, infrastructure automation, Docker, Kubernetes, and cloud deployment practicesStrong understanding of data quality, lineage, metadata management, governance, privacy, security, and regulatory complianceExperience with platform modernization, performance engineering, cost optimization, and reliability improvementPractical experience integrating AI or LLM capabilities into production data platforms or engineering workflowsExposure to RAG, vector databases, Agentic AI, LLM orchestration, Vertex AI, or Model Context ProtocolExperience mentoring senior engineers and establishing engineering standards and best practicesAbility to influence multiple teams without relying on formal authorityStrong stakeholder-management and executive-communication skillsA demonstrated ability to connect technical architecture decisions to measurable business outcomes