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 setting 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 ex
📌 Data Platform Architect (India)
🏢 TaskUs
📍 India