Engineering Manager

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, messag

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, message-driven patterns, and related engineering practices.

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. Lead engineering across high-volume 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. 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. 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. 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. 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. 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.