Lead Engineer – Role & Responsibilities
As a Lead Engineer, you will provide technical leadership and architectural direction while remaining hands-on in building scalable, resilient, and production-grade systems. You will play a critical role in shaping engineering standards, driving modernization, and enabling intelligent, AI-powered capabilities across the platform.
Key Responsibilities
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Technology Evaluation & Innovation
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Evaluate emerging technologies and contribute to architectural decision-making, considering alignment with Target’s technical ecosystem, long-term maintainability, scalability, and total cost of ownership.
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Lead research initiatives and proof-of-concept efforts to validate new tools, frameworks, and platforms before adoption.
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Architecture & Engineering Excellence
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Design and own scalable, secure, high-performance architectures.
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Establish and evolve engineering standards and best practices in complex or ambiguous environments.
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Lead service design, lifecycle management, and overall technical governance of team-owned platforms.
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Ensure code quality, infrastructure standards, and long-term sustainability of services.
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Hands-On Development & Delivery
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Contribute directly to development efforts, particularly on complex and high-impact components.
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Ensure solutions are production-ready, deployable, resilient, and scalable.
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Drive implementation quality through strong testing, automation, and CI/CD practices.
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Enterprise Impact & Thought Leadership
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Provide technical thought leadership to promote reusable components and consistent architectural patterns.
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Participate in planning and designing services with enterprise-wide impact.
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Guide the team in resolving routine and moderately complex technical challenges, escalating risks when necessary.
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Observability & Reliability Engineering
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Champion a culture of observability and operational excellence.
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Ensure monitoring, logging, and metrics are embedded into services.
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Leverage operational data to continuously improve system stability, performance, and reliability.
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Align monitoring strategies with organizational observability principles.
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AI & Intelligent Systems Integration
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Integrate Large Language Models (LLMs) and Generative AI capabilities into core applications.
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Design and implement safeguards to mitigate hallucinations and improve AI reliability.
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Build and scale agentic frameworks and AI-driven automation solutions to enhance business processes.
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Data Governance & Platform Optimization
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Influence and evolve data standards, policies, and governance practices.
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Configure, optimize, and monitor data management platforms with minimal oversight.
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Identify performance and efficiency improvements across data systems.
Required Skills & Expertise
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Advanced proficiency in Java, including backend systems and automation workflows
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Strong experience in Microservices Architecture and distributed systems
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Expertise with Spring Boot or Micronaut, including reactive programming models
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Hands-on experience with AI/LLM integration and GenAI-enabled applications
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Experience with Messaging Systems such as Kafka or RabbitMQ
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Proficiency with Databases, including NoSQL (Cassandra, MongoDB) and SQL (PostgreSQL)
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Experience building and managing CI/CD pipelines (Jenkins, GitLab, or similar)
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Strong background in Unit & Integration Testing (JUnit, Spock, TestContainers)
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Experience with Cloud Platforms (AWS, GCP, Azure)
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Expertise in Containerization & Orchestration (Docker, Kubernetes)
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Strong understanding of Monitoring & Observability tools (Grafana, ELK, Prometheus)
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Solid grasp of Event-Driven Architecture patterns in distributed environments
Preferred Qualifications
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Proficiency in Python or Kotlin
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Experience with Legacy System Modernization and refactoring initiatives
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Knowledge of Security Best Practices, including OWASP standards and secure coding principles
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Familiarity with Agile methodologies such as Scrum or Kanban
Level:
6
