1. Platform Architecture & Roadmap
Own the technical vision and evolution of the enterprise data platform aligned to business and digital strategy.
Define the target architecture for real-time and batch data processing, including Medallion architecture patterns and event-driven pipelines.
Translate platform strategy into quarterly technical objectives, capacity plans, and measurable engineering outcomes.
2. Core Data Platform Capabilities
Design and operate the enterprise data storage using Amazon S3 and Apache Iceberg.
Implement and mature Bronze–Silver–Gold (Medallion) architecture for structured, governed data processing.
Define and maintain reusable building blocks for ingestion, transformation, storage, and serving.
Establish reference implementations for streaming (Lambda/Flink) and batch ETL (Spark/Flink).
3. Enablement & Data Engineering Experience
Provide standardized pipelines, templates, and patterns to enable analytics, applications, and ML teams to consume data efficiently.
Enable self-service access to trusted datasets through Athena/Trino, APIs, and ML pipelines.
Partner with application, edge, and ML teams to ensure seamless integration from telemetry to business-ready data.
4. Reliability, Security & Compliance
Enforce enterprise-grade security and governance using Lake Formation, IAM, and KMS.
Implement data quality, validation, lineage, and observability controls across pipelines.
Design for resilience through robust error handling, reprocessing, and backfill mechanisms.
Establish and maintain automated unit testing for data transformations, schemas, and business logic.
Support System Integration Testing (SIT) and User Acceptance Testing (UAT).
5. Performance, Cost & Scalability
Design and Optimize storage, compute, and query performance for cost efficiency and predictable scaling.
Monitor and continuously improve platform throughput, latency, and unit economics (e.g., cost per GB, cost per pipeline).
6. Cross-Functional Leadership & Stakeholder Engagement
Work closely with edge, application, analytics, and AI teams to translate raw telemetry into trusted business insights.
Partner with architecture, security, and cloud teams to align platform implementation with enterprise standards.
Act as a technical advisor to product and business stakeholders on data platform capabilities and constraints.