Key Responsibilities:
Own end-to-end delivery for the group – roadmap, execution, and Agile/SAFe process.
Hire, mentor, and grow a team of engineers.
Guide architecture and technical direction for scalable data pipelines (real-time and batch), streaming (Kafka), and processing frameworks (Spark/Flink).
Oversee the platform's AWS infrastructure and cloud architecture decisions.
Ensure sound data storage design across relational, NoSQL, and search systems.
Drive improvements in reliability, observability, and cost efficiency.
Guide the team's development of an AI agent for forensic use cases, including MCP tool design for safe, effective data access.
Represent the group across a matrixed org and partner with product teams.
Requirements :
10+ years of hands-on engineering experience, including 5+ years managing engineering teams, ideally in a matrixed organization.
Strong background in Data Platform or Big Data engineering within a cloud-native, high-scale environment, with experience designing and operating large-scale, data-intensive systems.
Strong understanding of distributed systems and data platform concepts, including scalability, partitioning, consistency, fault tolerance, performance, and reliability.
Sufficient hands-on technical and coding depth to guide architecture, review designs and code, and troubleshoot complex production issues.
Working knowledge of Java/Spring Boot, Kafka or similar streaming technologies, and Kubernetes.
Solid experience with AWS/Azure/GCP and etc.
Strong experience with PostgreSQL/RDS, OpenSearch/Elasticsearch, and NoSQL databases, including the ability to evaluate architectural trade-offs.
Working knowledge of AI agent architectures and MCP (Model Context Protocol) or similar standards.
Strong communication skills and sound judgment under pressure
Advantages:
Observability tooling; MLOps exposure.
Hands-on experience building AI agents or MCP servers.
