Engineering Manager

Role Overview

As an Engineering Manager – Full Stack Data Platform (Backend heavy with Frontend

Familiarity) at Bidgely, you will lead and grow a high-performing engineering team

responsible for building and operating the full stack of Bidgely's cloud-native applications

and services. You will bridge the gap between technical execution and organizational

leadership — driving engineering excellence while ensuring your team delivers scalable,

reliable, and impactful products.

This role requires strong hands-on technical depth across full backend development with

familiarity into FE (Java, Python, Agentic AI and modern front-end and back-end stacks),

combined with the leadership maturity to inspire, manage, and develop a motivated team of

engineers.

We are looking for a true “player-coach” — a highly motivated, go-getter Engineering

Manager who instinctively reads the needs of the team and steps in to fill the gap, whether

that means writing production code, unblocking a critical deployment, decoding a legacy

system, or guiding an engineer through a tough technical challenge. You do not wait to be

asked — you see what needs to be done and you do it.

Reporting to the Head of Data Engineering, you will:

● Lead and mentor a team of engineers across backend, frontend, GenAI engineers, MLOps

and Data platform layers.

● Own end-to-end delivery of engineering initiatives across Bidgely's cloud-hosted application

stack.

● Implement technical strategy, architectural decisions, and drive engineering best practices

across the team.

● Manage project delivery, sprint planning, roadmap execution, and stakeholder

communication.

● Champion the adoption of AI, LLM, and Agentic AI capabilities within Bidgely's product and

engineering workflows.

● Collaborate closely with Product, Data Science, Infrastructure, and QA teams to deliver

high-quality, scalable solutions

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

● Drive legacy-to-modern migration — assess and decode existing JAR/WAR-based legacy

codebases and re-architect them into containerized, cloud-native microservices.

● Manage engineering workflows through Jira and Confluence with discipline — ensuring

sprint hygiene, clear documentation, and team-wide visibility without compromise.

This is a high-impact role that combines deep technical engagement with people leadership

—ideal for someone who loves building great products and great teams.

Key Responsibilities

Engineering Leadership & People Management

● Lead, manage, and grow a team of full stack engineers — setting a high bar for technical

quality, ownership, and collaboration.

● Conduct regular 1:1s, provide meaningful feedback, and create individualized growth plans

for team members.

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

● Recruit, interview, and onboard top engineering talent to expand team capabilities.

● Shield the team from organizational noise while ensuring alignment with business priorities.

● Recognize and celebrate technical achievement, enabling engineers to do their best work.

Full Stack Technical Leadership

● Provide hands-on technical guidance across backend (Java, Python), frontend, APIs, and data

layers.

● Drive architectural decisions for scalable, maintainable full stack systems deployed on cloud

infrastructure.

● Establish and enforce engineering standards: code quality, testing, documentation, and peer

review practices.

● Lead technical design reviews and ensure engineering solutions are performant, secure, and

production-ready.

● Stay current with evolving full stack technologies and evaluate new tools and frameworks

relevant to Bidgely's stack.

● Identify and address technical debt strategically without sacrificing delivery velocity.

● Decode and modernize legacy codebases — understand JAR/WAR-based deployment models,

legacy architectures, and event-driven or distributed systems, and lead the re-platforming to

containerized, cloud-native deployments on AWS.

● Apply and enforce SOLID principles, design patterns, and distributed systems best practices

across the codebase — ensuring maintainability, scalability, and engineering quality.

● Conduct thorough code reviews — set the quality bar, provide actionable feedback, and

actively unblock team members by jumping in as a senior contributor when needed.

● Provide clarity and technical direction to both junior and senior engineers — bridging skill

gaps, mentoring on complex problem-solving, and ensuring every team member has a clear

path to meet their goals.

Project & Delivery Management

● Own end-to-end delivery of engineering projects — from requirements clarification and

technical scoping to release and post-launch monitoring.

● Lead sprint planning, backlog grooming, and milestone tracking in partnership with Product

Management.

● Proactively identify risks, dependencies, and blockers — and drive timely resolution.

● Maintain clear, concise communication with engineering leadership and cross-functional

stakeholders on delivery status and trade-offs.

● Drive delivery velocity improvements through process optimization, automation, and tooling

enhancements.

Cloud-Native Application Development

● Guide the team in building and operating full stack applications on cloud platforms (AWS,

Azure, and/or GCP).

● Ensure engineering best practices for cloud-native development: microservices,

containerization, CI/CD, and infrastructure-as-code awareness.

● Partner with the Infrastructure team to define service contracts, deployment patterns, and

operational standards.

● Drive observability practices within application layers: structured logging, distributed tracing,

and alerting.

● Ensure services meet reliability, scalability, and security standards appropriate for enterprise

deployments.

AI, LLM & Agentic AI Enablement

● Champion the adoption of AI and LLM capabilities within engineering workflows — including

code generation, testing automation, and developer productivity tooling.

● Collaborate with Data Science and Product teams to integrate LLM-powered features and

agentic workflows into Bidgely's product stack.

● Guide engineers in evaluating and adopting AI-assisted development practices responsibly

and effectively.

● Stay current with the evolving landscape of generative AI, LLM frameworks (e.g., LangChain,

Strands), and Agentic AI architectures.

● Drive engineering readiness for AI-powered product capabilities, ensuring teams understand

integration patterns, latency, reliability, and safety considerations.

Cross-Functional Collaboration

● Act as the primary engineering liaison with Product, Data Science, Design, QA, and

Infrastructure teams.

● Participate in roadmap planning and ensure engineering input informs product prioritization

decisions.

● Support customer-facing technical discussions where engineering depth is required.

● Collaborate with the security team to ensure application-layer security standards are

embedded in engineering processes.

Engineering Execution & Tooling

● Own Jira and Confluence as essential engineering tools — maintain sprint boards, backlog

hygiene, clear ticket definitions, and up-to-date documentation without compromise.

● Ensure every engineer has clarity on their tasks, priorities, and acceptance criteria —

eliminating ambiguity so the team can move fast with confidence.

● Drive continuous improvement in engineering processes — retrospectives, sprint reviews,

capacity planning, and delivery forecasting done consistently and transparently.

Success Metrics

Success in this role will be measured across three dimensions:

Team Health & Growth

● High team engagement, retention, and satisfaction scores.

● All engineers have clear growth plans and are progressing against them.

● Effective hiring to plan — filling open roles with high-quality talent on schedule.

Engineering Delivery

● Consistent on-time, high-quality delivery against sprint commitments and quarterly

milestones.

● Reduction in escaped defects and production incidents attributable to engineering quality.

● Measurable improvement in delivery lead time, cycle time, and deployment frequency.

Technical Excellence

● Improving codebase health metrics: test coverage, code review turnaround, and

documentation quality.

● Successful adoption of AI/LLM-assisted development tools that improve team productivity.

● Engineering systems that meet reliability, scalability, and security targets in production.

Qualifications & Experience

Educational Background Bachelor's degree in Computer Science, Software Engineering, or a

related field; Master's degree a plus.

Professional Experience

● Must Have: 8+ years of software engineering experience, with at least 2–4 years in an

Engineering Manager or Tech Lead Manager role.

● Strong hands-on background as a full stack engineer — proficiency in Java, Sprint Boot, Spark,

React JS and Python is required; experience with additional languages or frameworks

(Node.js, Go, React, etc.) is a plus.

● Demonstrated experience managing and growing engineering teams of 5–15 engineers.

● Proven track record of delivering complex, multi-team software projects on time and at

quality.

● Experience building and operating cloud-native applications on AWS, Azure, and/or GCP.

● Strong project management skills — comfortable with Agile/Scrum methodologies, sprint

planning, and stakeholder management.

● Preferred: Hands-on experience integrating LLM APIs, building agentic workflows, or applying

AI/ML capabilities within product engineering.

● Preferred: Experience in regulated industries or enterprise SaaS environments.

● Must Have: Proven ability to read and decode legacy codebases (JAR/WAR deployments,

monolithic architectures) and lead the transition to containerized microservices using Docker

and Kubernetes.

● Must Have: Deep understanding of event-driven and distributed system architectures —

messaging patterns, queuing systems, eventual consistency, and fault tolerance.

● Must Have: Highly motivated self-starter with a go-getter attitude — proactively identifies

gaps in the team or process and takes ownership to fill them without waiting to be directed.

● Must Have: Hands-on proficiency with Jira and Confluence — experience owning sprint

ceremonies, maintaining clean backlogs, and enforcing documentation standards across the

team.

Technical & Domain Expertise

● Strong proficiency in backend development: Java (Spring Boot, microservices), Python

(FastAPI, FastMCP, Flask, or equivalent).

● Solid understanding of frontend technologies (React, Angular, or equivalent) and API design

(REST, GraphQL).

● Experience with cloud-native development patterns: containerization (Docker, Kubernetes),

CI/CD pipelines, and serverless architectures.

● Familiarity with relational and NoSQL databases, data modeling, and query optimization.

● Working knowledge of cloud platforms: AWS, Azure, and/or GCP — services, deployment

models, and cost management.

● Understanding of software security principles, authentication/authorization patterns, and

OWASP best practices.

● Exposure to AI/ML integration patterns, LLM frameworks (LangChain, LlamaIndex, OpenAI

API), and Agentic AI architectures (strong advantage).

● Familiarity with observability tools: Datadog, Prometheus/Grafana, ELK Stack, or equivalents.

● Experience with legacy Java application packaging and deployment — JAR/WAR files,

application servers (Tomcat, JBoss, WebLogic), and migration to containerized deployment

pipelines.

● Strong command of SOLID principles, design patterns (GoF), and distributed system

fundamentals — including event-driven architectures, message brokers (Kafka, RabbitMQ),

and service orchestration.

Core Competencies

● Technical Depth with Leadership Breadth — Hands-on enough to earn team respect and

guide architecture; experienced enough to lead people, manage complexity, and drive

outcomes.

● People-First Leadership — Creates an environment where engineers feel valued, grow

professionally, and do their best work.

● Delivery Ownership — Drives end-to-end accountability for project execution without

micromanaging; proactively removes blockers.

● AI & LLM Fluency — Understands generative AI, LLM integration patterns, and agentic

architectures well enough to guide engineering adoption and product integration decisions.

● Cross-Functional Communication — Translates technical complexity into clear business

language for Product, Leadership, and stakeholders.

● Growth Mindset — Continuously learning, comfortable with ambiguity, and energized by

fast-moving, high-impact environments.

Why Join Bidgely?

● Opportunity to shape the future of clean energy and sustainability.

● Work in a dynamic, fast-paced environment with diverse, cross-functional teams.

● Competitive compensation, benefits, and opportunities for professional growth.

● Make a tangible impact at the intersection of AI, data, and climate tech.

Diversity, Equity, Inclusion and Equal Opportunity

At Bidgely we’re on a mission to make a difference and build a workplace where every unique voice

is heard and celebrated. Here, we believe that celebrating the unique backgrounds, perspectives

and abilities of every employee makes an impact not only for our company internally but also for

our clients, customers and community. We are an equal opportunity employer and believe that the

inclusive atmosphere we build together will enable every person to grow, contribute and thrive.

Our hiring decisions are based on your skills, talent, and passion – not on your background, gender,

race, age, or the quirky way you dance at office parties