Engineering Manager

• 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 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.

• 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.
• Fix recurring issues at the root so the team spends its time building, not firefighting.

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…