Principal Data Architect – Modern Data Platforms & Data Mesh

McAfee • San Jose, CA

19h • Hybrid

About The Position

Strategic Architecture & Data Mesh: Lead the design, evolution, and implementation of a scalable, decentralized Data Mesh architecture. Define domain boundaries, data products, and federated governance standards to enable self-service analytics across the organization. Hands-on Engineering Leadership: Provide expert-level, hands-on technical leadership in building foundational data frameworks. Architect and prototype resilient data pipelines using Databricks, Snowflake, and Spark , ensuring high availability and low latency. Modern Data Storage & Formats: Drive the adoption of open table formats ( Delta Lake, Apache Iceberg ) to ensure ACID compliance, time travel, and schema evolution capabilities across the data lakehouse. Data Security & Governance: Architect advanced data security patterns, including dynamic data masking , tokenization , and row-level security. Implement and manage centralized discovery and access control using Unity Catalog or similar enterprise data catalogs. Real-Time & Event-Driven Systems: Design and deploy high-throughput streaming architectures using Kafka or Pulsar for real-time data ingestion and processing. MLOps & Analytics Integration: Collaborate closely with data scientists to operationalize machine learning models. Build robust MLOps pipelines and feature stores that bridge the gap between data engineering and production AI/ML. Mentorship & Culture: Mentor and guide a team of senior data engineers and architects, fostering a culture of technical excellence, automation (CI/CD), and "Data as a Product" thinking. Operational Excellence: Troubleshoot complex performance bottlenecks in distributed systems and establish best practices for cost optimization and observability (Data Observability) in cloud environments.

Requirements

• 10+ years of professional software and data engineering experience, with a significant portion in a technical leadership or architectural role.
• Data Mesh Expertise: Proven track record of transforming monolithic data warehouses into decentralized Data Mesh architectures, including experience with data contracts and federated governance.
• Modern Platform Proficiency: Deep, hands-on expertise with modern data platforms such as Databricks and Snowflake , including optimizing compute clusters and managing costs.
• Big Data Ecosystem: Strong proficiency in distributed computing frameworks (Apache Spark) and open standards ( Delta Lake, Iceberg, Hudi ).
• Security First: Extensive experience implementing data privacy regulations (GDPR, CCPA) via technical controls like data masking, tokenization, and encryption at rest/in transit.
• Coding & Scripting: Expert-level proficiency in Python, SQL, and Scala/Java . You treat data pipelines as code, strictly adhering to CI/CD , unit testing, and version control best practices.
• Streaming & Orchestration: Deep understanding of event-driven architectures ( Kafka , Kinesis) and workflow orchestration tools (Airflow, dbt, Dagster).
• Catalog & Discovery: Hands-on experience implementing data discovery and governance tools such as Unity Catalog , Alation, or Collibra.
• MLOps Familiarity: Strong understanding of the end-to-end machine learning lifecycle, including model registry, feature stores, and model serving

Responsibilities

• Lead the design, evolution, and implementation of a scalable, decentralized Data Mesh architecture.
• Define domain boundaries, data products, and federated governance standards to enable self-service analytics across the organization.
• Provide expert-level, hands-on technical leadership in building foundational data frameworks.
• Architect and prototype resilient data pipelines using Databricks, Snowflake, and Spark , ensuring high availability and low latency.
• Drive the adoption of open table formats ( Delta Lake, Apache Iceberg ) to ensure ACID compliance, time travel, and schema evolution capabilities across the data lakehouse.
• Architect advanced data security patterns, including dynamic data masking , tokenization , and row-level security.
• Implement and manage centralized discovery and access control using Unity Catalog or similar enterprise data catalogs.
• Design and deploy high-throughput streaming architectures using Kafka or Pulsar for real-time data ingestion and processing.
• Collaborate closely with data scientists to operationalize machine learning models.
• Build robust MLOps pipelines and feature stores that bridge the gap between data engineering and production AI/ML.
• Mentor and guide a team of senior data engineers and architects, fostering a culture of technical excellence, automation (CI/CD), and "Data as a Product" thinking.
• Troubleshoot complex performance bottlenecks in distributed systems and establish best practices for cost optimization and observability (Data Observability) in cloud environments.

Benefits

• Bonus Program
• 401k Retirement Plan
• Medical, Dental, Vision, Basic Life, Short Term Disability and Long-Term Disability Coverage
• Paid Parental Leave
• Support for Community Involvement
• 14 Paid Company Holidays
• Unlimited Paid Time Off for Exempt Employees
• 96 Hours of Sick Time and 120 Hours of Vacation for Non-Exempt Employees Accrued Each Year

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed