Data Platform Architect - Apache Iceberg Lakehouse

Position Summary

We are seeking a hands-on Principal Data Platform Architect to lead the design, implementation, and operationalization of an enterprise Apache Iceberg Lakehouse platform.

This role combines enterprise architecture with deep platform engineering experience to build a scalable, cloud-native data platform supporting analytics, AI/ML, GenAI, streaming, and governed data products.

Key Responsibilities:

• Define enterprise Lakehouse architecture, standards, governance, and implementation roadmap.
• Design and build Apache Iceberg-based bronze, silver and gold data layers.
• Develop ingestion frameworks for batch, streaming, CDC, APIs, and event-driven integrations.
• Build scalable Spark, Flink, Kafka, dbt, SQL, and Python pipelines.
• Implement metadata management, cataloging, lineage, RBAC, encryption, and data quality.
• Optimize partitioning, compaction, snapshots, metadata, and query performance.
• Enable AI-ready semantic layers, feature engineering, RAG, and GenAI workloads.
• Implement Infrastructure as Code, CI/CD, monitoring, and observability.

Required Skills:

• Apache Iceberg, Spark, Flink, Kafka, dbt, Trino, Snowflake, Databricks
• AWS, Azure, or Google Cloud
• Python, Scala, SQL, Java
• Kubernetes, Docker, Terraform
• Airflow, Dagster, or Prefect
• Polaris, Unity Catalog, Glue Catalog, Apache Nessie

Preferred Qualifications:

• 12+ years designing enterprise data platforms.
• Experience implementing Lakehouse architectures at enterprise scale.
• Experience with petabyte-scale environments and AI/ML platforms.

Success Measures:

• Deliver a production-ready Apache Iceberg Lakehouse.
• Establish governance, data quality, and enterprise standards.
• Enable self-service analytics and AI-ready governed data products.