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