Engineering Manager

Pod Engineering Manager: ecommerce platform Pod Engineering Manager to anchor a cross-functional product pod within our B2B eCommerce platform. This pod ships customer outcomes - not just code - using AI-native workflows, continuous delivery, and human-centered engineering craft. You will write code, review pull requests, debug production issues, and pair with engineers daily - not manage from a distance.

We expect you to spend a significant portion of your time in the codebase alongside your pod, leading by example and maintaining the technical credibility that earns your team's trust. In our pod model, product management, design, and engineering sit together as one autonomous unit that owns a product surface end-to-end, from discovery through production observability. Your pod will work on core commerce capabilities - product catalog, pricing and quoting, order management, payment processing, ERP integrations, or buyer experience - depending on the pod's domain.

As the pod's technical leader, you'll champion AI-augmented development practices - AI pair-programming, automated code review, LLM-assisted testing, AI-driven incident triage - while keeping humans accountable for architecture decisions, customer empathy, and code quality. You unblock, coach, and elevate - with your hands on the keyboard. You do not assign tickets from the sidelines.

What You'll Do Write code and stay hands-on Actively contribute to the pod's codebase — building features, fixing bugs, tackling complex integrations, and taking on technical spikes. Use AI coding tools to amplify your own output. Your hands-on work sets the quality bar and keeps you connected to the real challenges your engineers face every day.

Lead pod delivery and health Facilitate lightweight rituals (standups, retros, demos) tuned to the pod's cadence — not prescribed ceremony. Ensure the pod ships incremental value every week through trunk-based development, feature flags, and zero-downtime releases. Champion AI-native engineering Drive adoption of AI coding assistants (GitHub Copilot, Claude Code, Cursor), LLM-powered test generation, and automated PR review.

Set guardrails so AI accelerates quality, not just velocity. Continuously evaluate emerging AI tools and integrate them into the pod's workflow. Own architecture and system design Set technical direction for the pod's product surface.

Conduct design reviews that account for current needs, future scale, and AI/ML integration points. Balance pragmatism with long-term soundness. In the commerce domain, this means designing for high-throughput transaction processing, complex pricing logic, multi-tenant catalog management, and reliable third-party integrations.

Drive cross-pod alignment Participate in cohort-level architecture syncs, API contract reviews, and shared platform decisions. Represent the pod's technical roadmap to leadership and ensure consistency across the product organization. Own production health Ensure the pod maintains SLOs, responds to incidents using AI-assisted triage, and feeds production learnings back into development priorities.

Build a culture of blameless postmortems and continuous operational improvement. Co-own the delivery pipeline Partner with platform engineering on the pod's CI/CD pipeline. Push toward trunk-based development, canary deploys, infrastructure-as-code, and comprehensive observability (logging, tracing, metrics).

Grow people Coach engineers through pairing, async code reviews, and stretch assignments. Develop AI fluency across the pod. Provide continuous, specific feedback rather than periodic reviews.

Build psychological safety and high engagement in a small, autonomous team. Translate between worlds Convert complex technical decisions into business language for product, leadership, and customer-facing teams - and translate customer pain back into engineering priorities. Experience & Technical Background: 10+ years building enterprise SaaS products, with at least 2 years leading or technically anchoring a cross-functional team (pod, squad, or similar autonomous-team model).

You are still coding regularly — this is not a slide-deck-and-meetings role. Current, demonstrable coding ability. You can pick up a complex ticket, ship production code, and conduct deep technical code reviews.

You lead from inside the codebase, not above it. Hands-on experience with AI development tools such as GitHub Copilot, Claude Code, Cursor, or equivalent. Comfort evaluating and integrating LLM-based capabilities into developer workflows and product features.

Strong technical foundation in the Java ecosystem - Java (11+), Spring Boot, Spring Cloud, and related frameworks. Experience with build tools like Maven or Gradle and familiarity with the JVM performance model. Proficiency in API design (REST and/or gRPC) using Spring MVC or JAX-RS.

Strong SQL and relational database skills (PostgreSQL, MySQL, or SQL Server) with experience in JPA/Hibernate. Comfortable with cloud platforms (AWS or Azure). Deep understanding of CI/CD , trunk-based development, infrastructure-as-code, observability toolchains, and feature-flag-driven release strategies.

Experience designing testable systems using TDD/BDD with JUnit, Mockito, or Test Containers. Familiar with contract testing for APIs (Spring Cloud Contract, Pact) and shift-left quality practices. Familiarity with AI-assisted test generation is a strong plus.

Track record of growing engineers through coaching and influence rather than directive management. Comfort working directly with product managers and designers. You understand outcome-based roadmaps, discovery techniques, and how to balance tech debt with customer value.

BS in Computer Science or equivalent practical experience. Firm grasp of data structures, distributed systems, and scalable software architecture. Nice to Have eCommerce Domain experience Experience working on a B2B eCommerce engine - order management, quoting workflows, multi-buyer account hierarchies, or storefront customization.

Familiarity with product catalog systems - taxonomy design, attribute management, search/faceting, and catalog syndication across channels. Hands-on experience with ERP integrations (Epicor Eclipse, Epicor P21, Infor SX.e, or similar distribution ERPs) - order sync, inventory feeds, invoicing, and master data management. Understanding of payment gateway integrations - PCI compliance considerations, tokenization, ACH/wire/credit card processing, and reconciliation workflows.

Exposure to B2B pricing complexity - contract pricing, tiered/volume discounts, customer-specific catalogs, or CPQ (configure-price-quote) systems. AI & Technical: Experience integrating LLM/AI capabilities into customer-facing product features (e.g., AI-powered product search, conversational buying assistants, intelligent order routing). Familiarity with prompt engineering, RAG architectures, or AI agent frameworks.

Experience with reactive/async Java (Project Reactor, WebFlux) or Kotlin as a JVM complement. Background in platform engineering, developer experience (DX), or internal tooling. Experience with event-driven architectures using Kafka, RabbitMQ, or similar, and microservices at scale.

Familiarity with containerization and orchestration (Docker, Kubernetes) in a Java service context. Contributions to open source or public technical writing. How We Work Pod model: S mall, autonomous, cross-functional pods each own a commerce domain end-to-end - catalog, checkout, integrations, buyer experience, etc.

No hand-offs between siloed teams. AI-first toolchain: AI coding assistants, automated code review, LLM-powered test generation, and AI-driven incident triage are part of daily work - not experiments. Continuous delivery: Trunk-based development, feature flags, canary deploys, and zero-downtime releases.

We ship small and often. Outcome-oriented: Pods are measured on shipped customer value, production health (SLOs), and team growth - not story points or lines of code. Continuous feedback: Coaching happens through pairing and async reviews, not annual performance cycles.

Why This Role Is Different In a traditional org, an engineering manager coordinates work across siloed teams and drifts away from the code. Here, you lead a small, empowered unit that owns discovery, delivery, and production for its product surface - and you stay hands-on while doing it. AI tools handle the repetitive - code scaffolding, test generation, incident triage, documentation - freeing you to focus on what humans do best: architecture judgment, team chemistry, customer empathy, and strategic trade-offs.

You're a manager who ships