Lead Java Backend Engineering

Role: Lead Java Backend Engineering

Location: Columbus, OH (Day 1 Onsite)

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.