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.
