Java Lead

Senior Engineer

This role requires:

Expert-level system design capability

Ability to handle complex business and technical requirements

Hands-on engineering expertise

Deep understanding of distributed systems

Strong clarity in architecture and communication

Leadership in influencing technical direction.

any

Key Responsibilities

1. A. End-to-End System Architecture

2. Architect complete systems from initial concept to production rollout.
3. Identify and define system boundaries, components, and service responsibilities.
4. Produce both high-level and low-level design artifacts (HLD, LLD).
5. Ensure architectures meet scaling, reliability, security, and performance goals.
6. Establish clear trade-offs for design decisions backed by solid engineering reasoning.

7. B. Component-Level Design

8. Break complex systems into independently deployable components.
9. Define APIs, communication contracts, schemas, and data models.
10. Apply modern architecture patterns (DDD, microservices, event-driven, CQRS, hexagonal).
11. Ensure clarity around responsibilities, dependencies, and interaction patterns.
12. Validate component-level design for maintainability and extensibility.

13. C. Communication Layers & Protocol Architecture

14. Design synchronous and asynchronous communication pathways:
• REST, RPC, GraphQL, gRPC
• Message queues (SQS, Pub/Sub)
• Streaming systems (Kafka, Pulsar, Kinesis)
15. Establish:
• Retry, timeout, and fallback strategies
• Circuit breakers and rate limiting
• Message ordering, partitioning, checkpointing
• Delivery semantics (at-least-once, exactly-once, at-most-once)
• Backpressure handling mechanisms
16. Implement contract versioning and evolution.

17. D. Data Architecture & Storage Strategy

18. Design data models, storage systems, and access patterns for:
• SQL databases
• NoSQL stores (document, key-value, columnar, graph)
• Distributed caches
• Blob/object stores
• Search indexes
19. Define:
• Sharding, partitioning, replication models
• Consistency and durability requirements
• Indexing, query optimization, storage lifecycle
• Backup/restore, schema evolution strategies
20. Architect real-time and batch data flows for large-scale processing.