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# Data Platform Architect - Apache Iceberg Lakehouse

- **Company:** APACHE LLC
- **Location:** United States
- **Contract:** Contract
- **Skills:** Query Performance, Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Business Analytics Applications, Apache HTTP Server, Microsoft Azure, Continuous Integration, Data Architecture, Data Infrastructure, Python (Programming Language), Metadata, Meta-Data Management, Role-Based Access Control, Cloud Services, Azure Machine Learning, Scala (Programming Language), SQL Databases, Data Streaming, Enterprise Data Management, Google Cloud, Feature Engineering, Retrieval-Augmented Generation, Snowflake, Apache Spark, Data Layers, Build Management, Kubernetes, Apache Flink, Apache Kafka, Terraform, Docker, Databricks
- **Published:** October 19, 2026
- **Apply:** https://www.dice.com/job-detail/e981a00f-eadf-46a6-96a4-7eb2bf4ff744

## About the Role

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.

## Description

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., 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.