Sr. Data Engineer

• 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, ConfigMaps, and Secrets management.