What the Architect will own:
- Platform Design & Research: Lead architectural decisions regarding compute engine selection, open-table format implementation, and tiered storage design.
- Agnostic Infrastructure: Architect a decoupled data environment that ensures interoperability across multiple engines and prevents proprietary vendor lock-in.
- Governance & Compliance: Design and oversee the implementation of automated data governance, including PII discovery, row/column-level security, and auditability.
- Standards & Frameworks: Define the Definition of Done for data pipelines, establishing coding standards, CI/CD patterns, and technical documentation requirements.
- Architectural Decision Records (ADR): Maintain a version-controlled repository of all consequential technical decisions, documenting the rationale, trade-offs, and long-term implications.
- Performance & FinOps: Monitor and optimize platform performance and spend, ensuring sub-second query speeds for massive user bases while maintaining a lean cloud footprint.
- Technical Stewardship: Conduct deep-dive code and design reviews for all data models and orchestration workflows; mentor and unblock senior engineering staff.
Technical Skills & Experience
- :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
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