Engineering Manager

- Role Overview

- Lead and grow a high-performing engineering team building and operating Bidgely's cloud-native applications and services.

- Bridge technical execution and organizational leadership, ensuring scalable, reliable, and impactful product delivery.

- Requires strong technical expertise in backend development (Java, Python, Agentic AI) with frontend familiarity, plus leadership skills to inspire and manage the team.

- Serve as a "player-coach," stepping in to code, unblock deployments, decode legacy systems, and mentor engineers as needed.

- Proactively identify team needs and actions, rather than waiting for instruction.

- Report to the Head of Data Engineering.

- Responsibilities include leading and mentoring engineers across backend, frontend, GenAI, MLOps, and Data platform layers.

- Own end-to-end delivery of engineering initiatives for Bidgely's cloud application stack.

- Drive technical strategy, architecture, and engineering best practices.

- Manage project delivery, sprint planning, roadmap execution, and stakeholder communication.

- Champion adoption of AI, LLM, and Agentic AI within products and engineering workflows.

- Collaborate with Product, Data Science, Infrastructure, and QA to deliver high-quality solutions.

- Foster a culture of ownership, continuous improvement, and psychological safety.

- Drive migration from legacy code (JAR/WAR) to containerized, cloud-native microservices.

- Manage workflows using Jira and Confluence for sprint hygiene and documentation.

- Combine deep technical engagement with people leadership.

Key Responsibilities

- Engineering Leadership & People Management

- Lead, manage, and grow a full stack engineering team focused on technical quality, ownership, and collaboration.

- Conduct regular 1:1s, give feedback, and create growth plans for team members.

- Foster a culture of curiosity, accountability, psychological safety, and learning.

- Recruit, interview, and onboard new engineering talent.

- Shield the team from distractions while aligning with business goals.

- Recognize technical achievements and enable engineers’ best work.

- Full Stack Technical Leadership

- Provide technical guidance across backend (Java, Python), frontend, APIs, and data layers.

- Drive architectural decisions for scalable, maintainable cloud-based systems.

- Enforce engineering standards: code quality, testing, documentation, peer review.

- Lead design reviews, ensuring solutions are performant, secure, and production-ready.

- Stay up to date with evolving full stack tech and evaluate new tools for the stack.

- Address technical debt strategically.

- Modernize legacy codebases and lead re-platforming to containerized AWS deployments.

- Enforce SOLID principles, design patterns, and best practices.

- Conduct code reviews and provide senior-level support as required.

- Mentor both junior and senior engineers, bridging skill gaps and guiding problem-solving.

- Project & Delivery Management

- Own engineering project delivery from scoping to post-launch monitoring.

- Lead sprint planning, backlog grooming, and milestone tracking.

- Identify and resolve risks, dependencies, and blockers proactively.

- Maintain clear communication with leadership and stakeholders on delivery status.

- Improve delivery velocity through process optimization, automation, and tooling enhancements.

- Cloud-Native Application Development

- Guide development and operation of full stack applications on cloud platforms (AWS, Azure, GCP).

- Ensure best practices in microservices, containerization, CI/CD, and infrastructure-as-code.

- Partner with Infrastructure for defining service contracts and operational standards.

- Drive observability: structured logging, tracing, and alerting.

- Ensure services meet reliability, scalability, and security standards.

- AI, LLM & Agentic AI Enablement

- Champion AI/LLM adoption in workflows: code generation, testing automation, productivity tools.

- Collaborate with Data Science and Product teams for LLM-powered product features.

- Guide responsible adoption of AI-assisted dev practices.

- Stay updated on generative AI, LLM frameworks (LangChain, Strands), Agentic AI architectures.

- Ensure engineering readiness for AI-powered product capabilities.

- Cross-Functional Collaboration

- Serve as main engineering liaison with Product, Data Science, Design, QA, and Infrastructure teams.

- Participate in roadmap planning and provide engineering input for product decisions.

- Support customer-facing technical discussions.

- Collaborate with security on application-layer security standards.

- Engineering Execution & Tooling

- Own Jira and Confluence for sprint management and documentation.

- Ensure engineers have clear task and priority visibility and acceptance criteria.

- Drive continuous improvement via retrospectives, sprint reviews, and capacity planning.

Success Metrics

- Team Health & Growth

- High team engagement, retention, and satisfaction levels.

- Growth plans for all engineers with measured progress.

- Effective, on-schedule hiring of top engineering talent.

- Engineering Delivery

- On-time, high-quality delivery of sprint and quarterly commitments.

- Reduction in escaped defects and production incidents.

- Improvement in lead time, cycle time, and deployment frequency.

- Technical Excellence

- Improvement in codebase health metrics (test coverage, code reviews, documentation).

- Adoption of AI/LLM-enabled development tools for improved productivity.

- Engineering systems meeting reliability, scalability, and security targets.

Qualifications & Experience

- Education: Bachelor's in Computer Science, Software Engineering, or related field (Master's a plus).

- Minimum 8 years software engineering experience, with 2–4 as Engineering Manager or Tech Lead Manager.

- Proficient as a full stack engineer: Java, Spring Boot, Spark, React JS, and Python (additional language experience a plus).

- Experience managing and growing engineering teams (5–15 engineers).

- Track record of delivering complex multi-team software projects.

- Cloud-native application experience (AWS, Azure, GCP).

- Strong project management, Agile/Scrum, and stakeholder management skills.

- Preferred: Hands-on LLM API integration, agentic workflows, or AI/ML in product engineering.

- Preferred: Experience in regulated industries or enterprise SaaS.

- Proven ability to read/decode legacy codebases (JAR/WAR, monolithic) and lead migration to containerized microservices (Docker, Kubernetes).

- Deep understanding of event-driven and distributed systems, messaging, queuing, consistency, and fault tolerance.

- Highly motivated self-starter; fills process gaps proactively.

- Hands-on proficiency with Jira and Confluence for sprint ceremonies, backlog, and documentation standards.

- Technical & Domain Expertise

- Strong backend proficiency: Java (Spring Boot, microservices), Python (FastAPI, Flask, or equivalent).

- Solid frontend and API design skills (React, Angular, REST, GraphQL).

- Experience with cloud-native patterns: containerization, CI/CD, serverless architectures.

- Familiar with relational/NoSQL databases, data modeling, and optimization.

- Working knowledge of cloud platforms, deployment models, and cost management.

- Understanding of software security, authentication/authorization, and OWASP best practices.

- Exposure to AI/ML and LLM frameworks (LangChain, LlamaIndex, OpenAI API); agentic AI architectures is a strong advantage.

- Familiarity with observability tools (Datadog, Prometheus, ELK Stack).

- Experience with legacy Java app packaging, deployment, and migration to containers.

- Command of SOLID principles, design patterns (GoF), distributed architecture, message brokers (Kafka, RabbitMQ), and orchestration.

- Core Competencies

- Deep technical skill with leadership experience.

- People-first leadership, promoting growth and best work.

- Ownership of delivery, removing blockers proactively.

- AI & LLM fluency for engineering and product integration.

- Ability to translate technical complexity for cross-functional stakeholders.

- Growth mindset, continuous learning, comfortable with ambiguity.

Why Join Bidgely?

- Shape the future of clean energy and sustainability.

- Work in a fast-paced, diverse, cross-functional environment.

- Competitive pay, benefits, and growth opportunities.

- Make an impact at the intersection of AI, data, and climate tech.

Diversity, Equity, Inclusion and Equal Opportunity

- Bidgely values unique backgrounds, perspectives, and abilities to benefit the company, clients, and community.

- Commitment to an inclusive workplace where all can grow and contribute.