Want to know if this job is worth applying to?
Bengaluru, Karnataka, India
Onsite
About the team / Role
As a Director, Data Platform Engineering, you will be the primary leader and visionary for the core data infrastructure that powers the entire enterprise. This is a "platform-as-a-product" role; your mission is to lead the team building the internal foundation—creating the high-performance engines, abstraction layers, and self-service frameworks that enable hundreds of other engineers to move faster. You will manage a team operating at the intersection of Systems Programming and Data Engineering, solving "hard-tech" problems like automated schema evolution, multi-engine compute optimization, and global data discovery. As a top-level technical leader, you will bridge the gap between long-term business strategy and engineering execution, ensuring WEX’s data platform remains a competitive advantage.
Is this role for you?
YES, if: You are an Engineering Manager or Lead Backend/Software Engineer who loves leading teams that solve "Big Data" problems, build APIs for data discovery, and optimize distributed systems.
NO, if: Your primary expertise is managing teams that write SQL queries, build Tableau dashboards, or manage ETL workflows without deep experience in Java or Python system architecture.
How you'll make an impact
Strategic Roadmap & Sovereignty: Define and execute the 3-5 year technical roadmap for the Data Lakehouse. You will guide your team in deciding how storage, compute, and metadata layers (e.g., Apache Polaris, Unity Catalog or Datahub Catalog) interact at an elemental level.
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.