Data Platform Architect

8+ Years in Data Engineering / Architecture: Proven experience delivering production-grade Lakehouse environments for high-concurrency (1,000+ user) organizations. Modern Data Stack Fluency: Extensive experience with Databricks (Lakehouse/Unity Catalog) and high-performance warehouses like Amazon Redshift or Snowflake. Open-Table Formats: Deep hands-on expertise with Apache Iceberg or Delta Lake, including optimization strategies for partitioning and schema evolution. Transformation & Modeling: Mastery of dbt (Core) for complex SQL-based modeling and PySpark or Python for sophisticated data processing. High-Efficiency Compute: Familiarity with vectorized/embedded engines like DuckDB for specialized or cost-sensitive processing tasks. Orchestration Mastery: Advanced experience with Apache Airflow, specifically in designing resilient, dependency-aware DAGs in resource-constrained environments. Cloud Ecosystems: Expert-level knowledge of AWS (S3, EC2, IAM) or equivalent services in Azure/GCP, with a focus on storage-compute separation. Experience with open-source catalog implementations like Apache Polaris. Knowledge of Data Ops principles and automated data quality testing frameworks. Experience translating technical debt and architectural roadmaps for C-level executives. Background in managing fixed-resource infrastructure (e.g., EC2/VM-based processing) vs. elastic serverless models. Kubernetes (K8s). Deep understanding of Pods, Deployments, Services,