Platform Engineering Lead- Data Services & Reporting

1. Cross-Cutting Platform Engineering Strategy & Alignment

2. Platform AI Capabilities & Wider AI Alignment

3. Production Estate Management & Technical Debt Elimination

4. Data Virtualization & Federation Industrialization

5. Reporting Platform & Experience Layer Integration

6. Technical Leadership & Mentorship

Required Technical Skillsets (Must be Hands-on)

• Java Software Engineering (Core Competence): Advanced, hands-on expertise in Java and enterprise Java frameworks (specifically Spring Boot , Spring Framework, and Hibernate/JPA). Proven track record of architecting, reviewing, and governing high-performance backend microservices, robust platform integration layers, and custom API layers.
• Data Federation & Query Optimization (Required Concepts): Deep understanding of distributed query execution, query plans, pushdown optimization, and logical data fabric/data virtualization concepts.
• BI & Reporting Platform Engineering: Strong architectural knowledge of enterprise BI and reporting platforms (e.g., Tableau , custom JavaScript/React web dashboards, Aspose , or similar reporting and document generation engines).
• AI-Assisted Development (Must-Have): Proven capability and hands-on experience using generative AI tools (e.g., GitHub Copilot, Claude, ChatGPT , or equivalent) to accelerate software delivery, write platform code, generate tests, and optimize development workflows.
• Enterprise Architecture & Implementation Design: Proven track record of designing and implementing high-scale, distributed, and resilient enterprise architectures bridging data storage, virtualization, APIs, and reporting in alignment with enterprise standards.
• API Design & Governance: Strong expertise in designing standardized REST/gRPC APIs, microservices, and secure data access patterns.
• Database Engineering & Security: Deep knowledge of relational databases (Oracle, SQL Server), distributed storage systems, and centralized data access governance frameworks (e.g., Apache Ranger).

Preferred / Nice-to-Have Skillsets

• Agentic AI & LLM Engineering (Highly Preferred): Conceptual or hands-on experience with Agentic AI frameworks and libraries (such as LangChain, LangGraph, Google ADK , or equivalent) to design, architect, and orchestrate autonomous AI agents, prompt engineering pipelines, and Retrieval-Augmented Generation (RAG) architectures.
• Data Virtualization Platforms (Highly Preferred): Deep hands-on or design experience with query federation engines such as Starburst, Trino, Presto, Denodo, Dremio, AWS Athena, or Apache Drill .
• AI Platform Engineering & LLM Architecture: Conceptual or hands-on understanding of engineering platform infrastructure to support AI workloads, integrating Large Language Models (LLMs), managing prompt engineering pipelines, vector databases, and deploying model hosting platforms.
• Modern Lakehouse Platforms: Experience working with cloud-native enterprise data lakehouse or warehouse platforms (e.g., Databricks, Snowflake , or equivalent).
• Distributed Compute Engines: Familiarity with Apache Spark (PySpark, Spark SQL) or Apache Flink for large-scale data processing.
• CI/CD & DevOps: Hands-on experience with modern DevOps toolchains (Jenkins, Tekton, GitLab CI), containerization (Docker, Kubernetes/OpenShift), enterprise workload automation ( Autosys ), and automated infrastructure-as-code (Terraform, Ansible).
• Production Observability Tools: Experience implementing and utilizing monitoring and observability toolsets (specifically Splunk, Prometheus, Grafana, AppDynamics , or similar) for enterprise-scale estate management.

Experience & Qualifications

• Total Engineering Experience: Minimum 12-15+ years of progressive experience in software engineering, platform engineering, or data architecture within a large-scale, highly regulated environment (preferably financial services).
• Technical Leadership: Minimum 5+ years in a senior engineering leadership, principal architect, or platform tech lead role, with a proven track record of driving large-scale platform modernization, rationalization, and technical governance across both data and reporting systems.
• Agile Engineering: Experience driving technical delivery and architectural alignment within Scrum/Kanban frameworks, collaborating closely with engineering teams.
• Education: Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related engineering field.
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