As a Principal Data Engineer at Equinix, you will be responsible for driving end-to-end delivery of enterprise-scale data solutions on Google Cloud Platform. This role combines hands-on technical expertise, requiring you to design sophisticated system architectures, lead cross-functional initiatives, and mentor data engineers. You will act as a trusted technical advisor for the team championing innovation across GCP, Data Engineering, AI, Agentic AI, and Model Context Protocol (MCP). The ideal candidate possesses unparalleled technical depth, exceptional strategic thinking, and a demonstrated history of delivering transformational data platform capabilities at a global scale.
System Design & Architecture Leadership
- Architect and evolve cloud-native, petabyte-scale data platforms and data products, with clearly defined and measurable SLOs for data freshness, availability, and reliability
- Design data solutions balancing scalability, performance, cost, and operational excellence through clear architectural trade-offs
- Establish reference architectures, architectural guardrails, and engineering standards adopted organization-wide
- Lead architectural governance across all data engineering squads, ensuring consistency, scalability, and security
- Evaluate and adopt emerging technologies (including Agentic AI/LLM and real-time systems) to improve engineering productivity and business outcomes
- Drive adoption of domain-oriented data architecture (e.g., data mesh principles) across business domains to enable ownership, consistency, and reuse
Technical Solutioning & Innovation
- Design scalable data solutions using modern cloud-native technologies (e.g., BigQuery, Dataflow, dbt/Dataform, Pub/Sub), selecting the right tools based on problem context
- Own end-to-end delivery of complex data engineering initiatives from design through production, and continuous evolution.
- for real-time streaming, event-driven architectures, and unified batch-streaming pipelines at petabyte scale
- Evaluate and adopt AI/LLM capabilities to improve engineering productivity and enable intelligent data consumption, using standard interfaces (e.g., Model Context Protocol) to integrate AI with data systems
- Establish engineering excellence frameworks covering performance engineering, cost governance, reliability, and observability
- Lead proof-of-concept and rapid prototyping of breakthrough technologies before scaling org-wide
Team Leadership & Mentorship
- Serve as a technical role model and multiplier — elevating the capabilities of Senior Staff, Staff, and Data Engineers across the organization
- Lead org-wide engineering communities of practice and knowledge-sharing initiatives
- Partner with engineering managers and HR to drive technical hiring strategy and raise the engineering talent bar
- Drive culture of engineering excellence, innovation, and continuous learning
- Establish mentorship programs that systematically grow the next generation of technical leaders
Data Governance & Compliance
- Architect automated data quality platforms, end-to-end lineage tracking, and enterprise metadata management systems
- Ensure compliance with GDPR, SOX, and emerging global data regulations through proactive architecture and policy design
- Lead privacy-by-design initiatives, zero-trust data access models, and data anonymization at scale
- Establish cross-cloud security standards and enforce them through automated policy guardrails
- Design privacy-preserving techniques, data anonymization, and secure data sharing mechanisms
Strategic Influence & Stakeholder Management
- Partner with business stakeholders to translate strategic objectives into scalable technical solutions
- Lead cross-functional initiatives spanning multiple engineering teams and business units
- Present technical concepts and architectural decisions to executive leadership
- Represent Equinix externally — at industry conferences, in open-source communities, and in strategic vendor partnerships
- Drive build-vs-buy decisions, vendor evaluation, and strategic technology partnerships at the enterprise level
Technical Expertise
- GCP Platform Mastery: 8+ years of hands-on experience with Google Cloud Platform, including deep expertise in BigQuery (advanced SQL, scripting, optimization, ML integration), Cloud Dataflow, Cloud Composer, Cloud Storage, Pub/Sub, Dataproc, and Vertex AI
- Programming Excellence: Expert-level Python/Java programming, proficiency in Python/Scala for Spark development
- Advanced Data Technologies: Deep expertise in Apache Beam, Apache Spark, Airflow, Kafka, and distributed computing architectures
- Infrastructure: Extensive experience with Terraform, CI/CD pipelines, and cloud infrastructure management
- Database & Data Modeling: Advanced knowledge of relational databases (PostgreSQL, MySQL) and NoSQL systems (BigTable, Firestore, MongoDB); expertise in data modeling, normalization, and performance optimization
- System Design: Proven ability to architect and deliver distributed, fault-tolerant, globally distributed data systems with rigorous security and cost governance
- AI/ML Integration: Deep experience with MLOps, feature engineering platforms, model serving infrastructure, and integrating LLM-powered capabilities into production data pipelines
Professional Experience & Leadership
- Master's or bachelor's degree in computer science, Engineering, or related field; Master's degree strongly preferred
- 15+ years of Data engineering experience with 8+ years specifically on Google Cloud Platform
- Proven track record of leading end-to-end delivery of complex, enterprise-scale data platform initiatives
- Experience designing and implementing data governance, compliance, and security frameworks
- Strong architectural thinking with ability to make sound technical decisions and trade-offs
- Proven ability to translate business requirements into scalable technical architectures and solutions
- Strong business acumen — ability to connect technical architecture decisions to measurable business outcomes
- Solve complex, ambiguous data platform challenges and drive decisions that improve system reliability, performance, and cost at scale
- Google Cloud Professional Data Engineer and Cloud Architect certifications
- Hands-on experience with Agentic AI frameworks, LLM orchestration, and Model Context Protocol (MCP) in production environments
- Published thought leadership — technical blog posts, conference talks, or open-source contributions in data engineering
- Experience with feature stores, model serving infrastructure, and AI/ML observability
- Understanding of data science workflows, statistical analysis, and advanced analytics
- Experience with BI and visualization tools (Looker, Tableau, Power BI)
- Contributions to industry standards bodies, open-source foundations, or emerging technology working groups
- Experience with multi-cloud or hybrid cloud architectures and data portability strategies
- Knowledge of data mesh, data fabric, or other modern data architecture patterns
Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing [this form](https://forms.office.com/pages/responsepage.aspx?id=cbKtcscv_kql7p3mpZ9r-50RYwLmhPpKtmEYPKOcl8hURTBBN0xMTllDRVc0VkJUSEYxMEk1V0NJQS4u&origin=lprLink).
Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
We use artificial intelligence in our hiring process. Learn more[here](https://careers.equinix.com/equinix-ai-guidance-for-candidates).
This posting is for a backfill position, meaning it is to fill an existing vacancy within our organization.
