Engineering Manager

Who Are We❓ Welcome to the world of Mrsool! 🌍✨ Where on-demand delivery meets unparalleled user needs to deliver anything you desire. As one of the largest delivery platforms in the Middle East and North Africa (MENA) region, Mrsool has captivated users with its unique and seamless experience, earning it the highest ratings among all major delivery platforms on both Apple's App Store and Google's Play Store. 🌟📲 What sets Mrsool apart is its commitment to providing an unmatched "order anything from anywhere" experience. 🌐📦 This extraordinary feat is made possible by our extensive fleet of dedicated on-demand couriers. With their unwavering dedication, they ensure that your desired items reach your doorstep, no matter where you are. 🚗🚲 Whether it's a late-night craving, a forgotten item, or a special gift for a loved one, Mrsool is here to deliver, quite literally. 😋🎁 We take pride in the convenience we offer, empowering you to get what you need when you need it, all at the tap of a button. 💪🏼💫 The Job in a Nutshell💡 We are seeking an experienced Engineering Manager to lead a team of 8 to 10 engineers building systems at the heart of Mrsool's marketplace — the real-time, high-scale services that power ordering, dispatch, payments, and the experiences our millions of users and couriers rely on every day. This is a hands-on, data-driven leadership role. You will own delivery and technical direction for your team while bringing sharp analytical judgement to problems that live at the intersection of distributed systems and applied data science. You will make a direct impact on reliability, unit economics, and the growth of the platform at scale. What You Will Do💡 Team Leadership: Lead, grow, and retain a team of 8–10 engineers. Own hiring, performance management, career development, and the health and delivery cadence of the team. Technical Direction: Set technical direction for your domain — driving RFCs, design docs (HLD/LLD), and execution plans, and holding a high bar for engineering standards. Data-Driven Decision Making: Ground team decisions in data. Define the right metrics, insist on measurement before and after changes, and use experimentation (A/B and quasi-experimental) to validate that changes actually move the numbers rather than shifting them around. Applied Data Science Partnership: Work fluently with data scientists and ML systems — forecasting, optimisation, personalisation, and prediction — enough to challenge modelling choices, reason about trade-offs, and integrate models reliably into production systems. System Architecture & Scale: Guide the design of highly concurrent, low-latency systems that process massive real-time throughput — event streams, microservices, and the data pipelines that feed them — while safeguarding data consistency and performance. Operational Excellence: Own reliability and root-cause discipline for your domain. Champion staging/E2E validation, observability (tracing/metrics), and CI/CD so that issues are caught before customers feel them, and diagnosed with data when they aren't. Cross-Functional Collaboration: Partner closely with product, operations, data science, and peer engineering squads to align on strategy and deliver seamless, well-instrumented experiences. Strategic Evolution: Balance short-term delivery against long-term architectural stability. Identify structural improvements to how the team measures, builds, and operates, and make the case for them with evidence. Requirements What Are We Looking For❓ - Years of Experience: 8+ years in software engineering, including 2+ years managing engineering teams, ideally in fast-paced product startups or high-scale tech enterprises. - Engineering Leadership: A track record of leading teams to ship reliable, high-scale backend/distributed systems, with strong judgement on hiring, delivery, and raising the technical bar. - Data Science & Analytical Strength: Strong grasp of data science and statistics — experimentation and causal inference, forecasting, and optimisation/ML concepts — with the analytical ability to interrogate models and metrics rather than take them at face value. This is a core requirement, not a nice-to-have. - Distributed Systems Depth: Solid computer-science fundamentals across distributed systems, concurrency, event-driven architectures, and the trade-offs of the CAP theorem. - Technical Toolkit: Comfortable reading and reasoning about production code and data (SQL and a backend language such as Go, Python, or Ruby), and about the pipelines that feed models and metrics. - Architectural Mindset: A knack for first-principles problem-solving and a strong record of balancing Non-Functional Requirements like reliability, latency, scaling, and cost. - Iterative Mindset: A bias towards shipping early and iterating; a belief in small, incremental changes over large, multi-quarter undertakings. - Leadership & Ambiguity: Proven ability to navigate ambiguity, set clear technical and organisational expect