Principal Software Engineer - Data Engineering & Cloud Architecture

Equinix; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Equinix is seeking a Principal Data Engineer to drive end-to-end delivery of large-scale data solutions on Google Cloud Platform. You will design architectures, mentor engineers, and guide cross-functional initiatives spanning data engineering, AI, and governance.

The role requires deep technical depth, strategic thinking, and a history of delivering transformational data capabilities at global scale. You will lead governance, security, and innovation initiatives while partnering with executives.

Qualifications

• Master's degree in Computer Science or related field.
• 15+ years of Data engineering experience with 8+ on Google Cloud Platform.
• Proven track record delivering enterprise-scale data platforms.

Responsibilities

• Architect and evolve petabyte-scale data platforms with measurable SLOs.
• Own end-to-end delivery of complex data initiatives from design to production.
• Lead data governance, security, and privacy by design across scales.
• Mentor data engineers and drive engineering excellence and best practices.

Skills

GCP Platform Mastery

Python/ Java

Apache Beam / Spark

Terraform / CI/CD

Data Modeling

Education

Master's degree in CS or related

Bachelor's degree in CS or related

Tools

Looker

Tableau

Power BI

Job description

Principal Software Engineer

Who are we? 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 bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future. 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.

Job Summary

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.

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.

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