Java Team Lead - Backend Engineering

Java Team Lead - Backend Engineering

Role Overview:
Lead a backend engineering team, combining technical expertise with people management. Drive delivery, innovation, and excellence in cloud-native architectures, container orchestration, observability, data processing, test automation, and AI/ML initiatives.

Key Responsibilities:

Manage and mentor a backend team, fostering technical ownership and collaboration.
Architect and implement scalable microservices (Java, Spring Boot) using SOLID principles.
Build event-driven systems with Apache Kafka.
Drive robust test automation strategies, ensuring high coverage and reliability.
Identify and automate manual processes to improve efficiency and reduce errors.
Ensure best practices: automated testing, CI/CD, observability, and secure coding.
Deploy cloud-native apps (AWS, Azure, GCP) using infrastructure-as-code.
Implement containerization and orchestration (Docker, Kubernetes).
Integrate observability tools (Prometheus, Grafana, Dynatrace, Splunk).
Lead/contribute to AI/ML projects with data scientists and ML engineers.
Design batch processing and ETL pipelines (Spring Batch, Apache Spark).
Use data visualization tools (Tableau, Power BI) for reporting.
Identify and resolve technical risks and performance issues.
Advocate for AI-powered development tools (e.g., GitHub Copilot).
Collaborate on architecture, standards, and delivery milestones.

Required Skills & Qualifications:

12+ years backend development (Java, Spring Boot) in distributed environments.
Advanced microservices, RESTful API, and service orchestration expertise.
Deep experience with Apache Kafka and distributed systems concepts.
Proven engineering team leadership and mentoring.
Experience with pair programming and collaborative development.
Extensive experience designing and implementing automated testing frameworks (unit, integration, contract, end-to-end).
Demonstrated history of automating manual workflows and processes.
Familiarity with AI-assisted development tools.
DevOps proficiency: Docker, Kubernetes, cloud-native deployments.
Hands-on with AWS, Azure, or GCP; infrastructure automation.
Experience with observability tools (Prometheus, Grafana, Dynatrace, Splunk).
Batch/data processing: Apache Spark, Spring Batch.
Data visualization: Tableau, Power BI, or similar.
Exposure to AI/ML projects and model integration.
Strong communication and stakeholder management.