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.