Backend Engineer Lead

10 - 17 years

14 - 24 Lacs

Posted:4 days ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Role & responsibilities

- Lead and mentor backend engineers while contributing code, reviews, and architectural guidance for scalable, resilient distributed services and data platforms.

- Architect and implement fault-tolerant microservices with patterns such as circuit breakers, bulkheads, idempotency, and saga/transactional outbox where appropriate.

- Embed application security into SDLC: threat modelling, secure design reviews, secure coding standards, dependency hygiene, and security test automation (SAST/DAST).

- Apply secure coding practices in Java/Spring: validation/encoding, parameterized queries, secrets management, method-level authorization, CSRF protections, and audit logging.

- Establish observability baselines for services: metrics, logs, traces, health checks, SLOs/SLIs, alerting, and incident response workflows.

- Drive end-to-end product development from scratch: from requirements and domain modelling to architecture, implementation, deployment, and iterative scaling.

- Partner with product, security, and compliance to meet fintech-grade reliability, security, and regulatory expectations across integrations and data flows.

- Champion dependency and vulnerability management, regular updates, and secure third-party/library usage policies.

Preferred candidate profile

- 10+ years in backend engineering with 2 to 3+ years leading teams or as a tech lead, delivering high-scale distributed systems in production.

Java, Spring Boot, and Spring Framework

- Proven experience designing and operating fault-tolerant microservices with strong consistency/availability trade-offs and resiliency patterns.

OWASP practices, including input validation, output encoding, authN/Z, session management

- Hands-on with observability tooling and practices for metrics, logs, traces, and alerting; defining SLOs and improving MTTR.

- Track record of building products from zero-to-one through scale, including data modelling, service decomposition, and operability.

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