Principal Data Engineer

Responsibilities

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

Qualifications

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

Preferred Qualifications

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.

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.

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This posting is for a backfill position, meaning it is to fill an existing vacancy within our organization.