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.