Platform-as-a-Product Delivery: Lead the development of internal SDKs, CLI tools, and automated orchestration frameworks. Your goal is to ensure your team abstracts away cloud complexity via Control Planes and Custom Operators, allowing Data Engineers to focus on business logic rather than infrastructure boilerplate.
Internal R&D Management: Champion and resource prototyping and benchmarking of emerging technologies (e.g., specialized Spark extensions) to keep the platform at the bleeding edge of performance and cost-efficiency.
Global Governance & Security: Oversee the architecture of "compliance-by-design" systems. Ensure the team automates data lineage, PII masking, and fine-grained access control across petabyte-scale environments without sacrificing developer velocity.
AI Governance Lake: Facilitate real-time data ingestion by implementing streaming support for Open Telemetry data from AI Agents into the Data Lake, drive the development of advanced AI evaluation metrics and reporting.
Engineering Excellence & Delivery: Set the gold standard for code quality and system design across the company. You will oversee Cross-Functional Architecture Reviews and ensure your team effectively resolves the most complex system outages or performance bottlenecks.
Team Leadership & Mentorship: Hire, mentor, and grow a world-class engineering team. Foster an "Engineering Community," influencing the hiring bar and professional development paths for the entire data engineering organization.
Experience you will bring
Experience: 15+ years in software engineering and distributed systems, with at least 4 years of experience managing high-performing engineering teams delivering platform-scale initiatives.
Core Technical Competencies (Software Engineering Focus)
Strong Software Foundations: Deep understanding of software engineering, system architecture, and scalable production applications (Algorithms, Data Structures, and System Design). Experience in the Java/J2EE ecosystem (Spring Boot, Microservices) and Python. We are looking for a leader who ensures their team writes clean, testable, and high-performance code, not just scripts.
Data as a Product: Experience managing or building the platforms / framework engines and APIs that power data movement, rather than just building the ETL/ELT pipelines themselves.
Data Lakehouse Mastery
Deep understanding of Apache Iceberg, Hudi, or Delta Lake (metadata management, manifest files, and compaction strategies).
Experience managing teams contributing to or deeply customizing open-source data projects (e.g., Spark, dbt).
Cloud & Infrastructure
Extensive experience with cloud architecture and services, including AWS (S3, EMR, Kubernetes, Lambda) and Azure.
Deep understanding of CI/CD automation, modern development tools, Git Actions, Terraform and frameworks.
AI-Driven Development & Productivity
AI Native Development: Experience leveraging AI Code Gen platforms into the software development lifecycle (SDLC) to automate code generation, reviews, generate unit tests, and perform root-cause analysis of system failures.
LLM-Ops for Platform: Ability to guide the architecture of infrastructure required to support AI Agent development by enabling vector database integration.
Leadership & Vision
Proven track record of driving adoption of new technologies across multiple autonomous teams.
Ability to communicate complex architectural trade-offs (e.g., "Latency vs. Consistency" or "Build vs. Buy") to C-suite executives and engineering teams alike.
Education
Bachelor’s or Master’s degree in Computer Science (Distributed Systems focus) preferred, or equivalent deep industry experience.
