Principal Data Architect

Location: San Jose or Newport Beach, CA

Department: External Positions

Location Type: HYBRID

Employment Type: FULL_TIME

• Lead the design and implementation of a scalable, decentralized Data Mesh architecture; define domain boundaries, data products, and federated governance standards
• Drive the organization from centralized to decentralized data management — change management experience is as important as technical depth
• Establish data contracts and self-service analytics capabilities across the organization

• Architect and prototype resilient data pipelines using Databricks, Snowflake, and Spark, ensuring high availability and low latency
• Drive adoption of open table formats (Delta Lake, Apache Iceberg) for ACID compliance, time travel, and schema evolution across the data lakehouse
• Troubleshoot complex performance bottlenecks in distributed systems

• Architect advanced data security patterns including dynamic data masking, tokenization, and row-level security
• Implement centralized discovery and access control using Unity Catalog or equivalent enterprise data catalogs
• Implement technical controls for data privacy regulations (GDPR, CCPA) including encryption at rest and in transit

• Design and deploy high-throughput streaming architectures using Kafka or Pulsar for real-time data ingestion and processing
• Deep understanding of workflow orchestration tools (Airflow, dbt, Dagster)

• Build robust MLOps pipelines and feature stores bridging data engineering and production AI/ML
• Collaborate with data scientists to operationalize machine learning models end-to-end

• Mentor senior data engineers and architects; foster a culture of technical excellence and "Data as a Product" thinking
• Treat data pipelines as code — strict CI/CD, unit testing, and version control practices expected

• 10+ years of software and data engineering experience, with a significant portion in technical leadership or architecture
• FAANG or Big Tech background strongly preferred
• Proven track record at large-scale enterprise environments — petabyte-scale data infrastructure is the baseline expectation
• Long tenure at key roles is a strong positive signal; we are not looking for contract-to-contract backgrounds or candidates with a pattern of 1–2 year stints
• Consulting backgrounds are not disqualifying but will be scrutinized — be prepared to speak specifically to what you owned vs. what you touched

• Data Mesh: proven experience transforming monolithic data warehouses into decentralized Data Mesh architectures including federated governance
• Platforms: deep hands-on expertise with Databricks and Snowflake including compute optimization and cost management
• Big Data: strong proficiency in Apache Spark, Delta Lake, Iceberg, and Hudi
• Coding: expert-level Python, SQL, and Scala/Java nice to have
• Streaming: Kafka or Kinesis; orchestration via Airflow, dbt, or Dagster
• Governance: hands-on with Unity Catalog, Alation, or Collibra
• MLOps: solid understanding of model registry, feature stores, and model serving