A well-known, high-growth e-commerce company operating across the GCC, serving a large customer base through high-traffic web and mobile platforms.
We are looking for an experienced Engineering Manager to lead engineering teams responsible for critical customer-facing e-commerce capabilities, including Catalog, Search, Pricing, Cart, Checkout, Promotions, Payments and related platform services. Each Engineering Manager owns a defined set of these domains.
This is a senior engineering leadership role for someone who combines strong people leadership, hands-on technical depth, architecture thinking, operational excellence and modern AI-assisted engineering practices. It is a hands-on role: you will challenge designs, understand production behavior and guide senior engineers.
Key Responsibilities
Engineering & Technical Leadership
- Lead and develop high-performing backend engineering teams building large-scale e-commerce services.
- Provide strong technical direction across Java-based distributed systems and microservices.
- Take an active part in architecture and system-design discussions, alongside architects and technical leads.
- Review and challenge designs for scalability, performance, resilience, security, maintainability and cost.
- Guide teams in designing clear service boundaries, APIs, asynchronous workflows, event-driven systems, caching strategies and data models.
- Identify architectural risks, technical debt, performance bottlenecks and scalability limits early, before they reach production.
- Balance pragmatic delivery with long-term platform quality, avoiding both over-engineering and short-term shortcuts.
E-commerce Domain Ownership
Lead engineering across high-volume customer-facing domains such as:
- Product Catalog
- Product Search & Discovery
- Pricing and Promotions
- Shopping Cart
- Checkout
- Payments
- Customer and Session Services
- Inventory availability integrations
- Order initiation and downstream integrations
You should understand the technical challenges behind large catalogs, high read/write volumes, concurrent shopping sessions, campaign traffic, flash-sale behavior, checkout consistency, pricing accuracy, inventory validation, payment reliability and customer experience.
Scalability, Performance & Reliability
- Establish measurable performance and reliability objectives (SLOs) for critical services.
- Improve API latency, throughput, database performance, caching efficiency and system capacity.
- Drive proper use of load testing, stress testing, profiling, capacity planning and performance benchmarking.
- Build systems resilient to partial failures through timeouts, retries, circuit breakers, idempotency, graceful degradation, dead-letter handling, and event replay and reconciliation.
- Make sure teams own their services in production, including on-call and peak campaign periods.
- Lead or actively take part in major production incident investigation, root-cause analysis and permanent corrective actions.
Engineering Excellence
- Raise engineering standards across design, coding, testing, deployment, documentation, security and observability.
- Build a strong code-review culture where every review improves engineering quality.
- Improve automated testing across unit, integration, contract, performance and critical end-to-end flows.
- Strengthen CI/CD and release practices to enable frequent and safe deployments.
- Establish clear engineering metrics: deployment frequency, lead time, change failure rate, mean time to recovery, production defects, service reliability, performance and technical debt.
- Fix recurring issues at the root so the team spends its time building, not firefighting.
AI-First Engineering Leadership
We already run AI agents for code review and production monitoring, and we expect our Engineering Managers to be strong practitioners of AI-assisted software engineering. You will:
- Use AI coding assistants extensively as part of daily engineering work.
- Be comfortable with agentic development workflows for prototyping, investigation, refactoring, testing, documentation and implementation.
- Understand how coding agents, LLMs, MCP-based tools, repository-aware agents and AI-assisted IDEs improve engineering productivity.
- Use AI to accelerate codebase discovery, debugging, test generation, code review, documentation and technical analysis.
- Understand the limitations of AI-generated code and keep security, architecture, quality and human review intact.
- Coach engineers on effective and responsible AI-assisted development.
- Continuously evaluate emerging AI engineering tools and introduce useful practices into the development lifecycle.
- Measure AI adoption by actual improvements in engineering productivity and quality, not by tool usage alone.
People Leadership
- Lead, coach and develop engineers and technical leads.
- Set clear expectations around ownership, technical quality, delivery and collaboration.
- Grow strong technical leaders within the team so that decisions do not depend on one person.
- Conduct meaningful performance discussions and create individual development plans.
- Identify performance gaps early and address them constructively.
- Participate actively in hiring and maintain a high technical hiring bar.
- Build succession plans for critical technical and leadership positions.
- Create a culture where engineers challenge ideas constructively, take ownership, learn continuously and understand the business impact of their systems.
Delivery & Product Partnership
- Partner closely with Product, QA, Architecture, DevOps/SRE, Data, Security and other engineering teams.
- Translate business requirements into technically sound and realistically executable engineering plans.
- Challenge requirements when they introduce unnecessary complexity, technical risk or poor customer experience.
- Improve estimation, planning, dependency management and release predictability.
- Balance new product development with reliability, performance improvements, technical debt and platform modernization.
- Communicate technical risks and trade-offs clearly to both technical and non-technical stakeholders.
- Own customer and business outcomes, well beyond tracking tickets or reporting delivery status.
Required Experience
- 10+ years of professional software engineering experience, with significant hands-on backend engineering experience.
- 3+ years in Engineering Manager, Technical Manager, Engineering Lead or equivalent leadership positions.
- Experience with large-scale, high-traffic transactional platforms. E-commerce, marketplace or retail technology experience is strongly preferred.
- Demonstrated experience with high traffic and concurrency, large product or transactional datasets, high-volume APIs, distributed services and mission-critical customer journeys.
- Strong experience with Java and Spring Boot.
- Strong understanding of microservices architecture, distributed systems, REST APIs, event-driven architecture, messaging and asynchronous processing, caching, database design and optimization, search technologies and cloud-native systems.
- Experience with technologies such as Kafka or similar messaging platforms, Redis, OpenSearch/Elasticsearch, SQL and NoSQL databases, AWS or a comparable public cloud, Docker/Kubernetes, CI/CD platforms and modern observability and APM tools.
Specific technology combinations are less important than demonstrated ability to design and operate large-scale production systems.
E-commerce Experience
Candidates should be able to discuss real production examples involving areas such as:
- Scaling catalog services containing large numbers of products and attributes
- Search performance and relevance
- Cache design and invalidation
- High-volume campaign or peak-period traffic
- Cart persistence and consistency
- Pricing and promotion accuracy
- Checkout concurrency
- Payment failures and reconciliation
- Distributed transaction challenges
- Inventory availability
- API latency and P95/P99 performance
- Production incidents and root-cause analysis
- Database and search-cluster scaling
We are particularly interested in candidates who can explain these problems with real numbers, architectural decisions, trade-offs, failures and lessons learned.
Leadership Profile
The ideal candidate:
- Is technically credible with senior engineers and can go deep without micromanaging implementation.
- Makes decisions using data and challenges architecture constructively.
- Understands both customer experience and backend engineering consequences, and can simplify complex systems.
- Creates strong engineers and future technical leaders, and sets a high bar for engineering quality.
- Communicates clearly with Product and Business stakeholders.
- Learns new technologies quickly and actively experiments with modern engineering and AI practices.
Education
- Bachelor's degree in Computer Science, Software Engineering, Computer Engineering or a related technical discipline, or equivalent practical experience.
- Master's degree in Computer Science, Software Engineering, Technology Management or a related field is an advantage.
What Success Looks Like
Within the first year, the Engineering Manager should be able to show:
- Peak campaigns delivered on owned services without a critical incident.
- Change failure rate and mean time to recovery improved against the starting baseline.
- An SLO, a dashboard and a named owner for every critical service.
- Stronger automated testing on critical flows, with more frequent and more confident releases.
- Better delivery predictability and a visible, shrinking technical debt backlog.
- A credible successor identified for each technical lead role.
- Measurable productivity and quality gains from AI-assisted engineering.
Most importantly, the Engineering Manager should leave the engineering organization technically stronger than they found it, with better systems, stronger engineers, stronger technical leaders and higher engineering standards.
