From Campus to Backend Engineer: 3 Years of Growth at JD
This article shares a recent graduate's three‑year journey at JD, offering practical advice for newcomers, detailing a large‑scale system redesign for a merchant conference, outlining promotion‑season preparation, and describing the development of an AI assistant while emphasizing continuous learning and professional mindset.
Graduating from the University of Chinese Academy of Sciences in 2021, the author joined JD as a fresh graduate and has been working on merchant content, AI assistants, and backend services.
Personal Growth Journey
The author reflects on common challenges for new engineers—lack of recognition, unfamiliar codebases, and communication hurdles—and recommends building trust, taking initiative on small tasks, and gradually tackling more critical responsibilities.
When assigned few tasks, proactively investigate alerts, logs, and even front‑end work to demonstrate value.
If existing code seems flawed, consider incremental refactoring and document changes for future maintainers.
For blocked cross‑team communication, suppress emotions, practice empathetic listening, and ask clear, focused questions.
System Redesign for JD Merchant Conference
In July 2022 the team faced a live‑streaming demand that the seven‑year‑old system could not handle. A domain‑driven redesign was undertaken, focusing on:
Defining clear business boundaries to improve efficiency and maintainability.
Controlling change through unified terminology, business abstraction, and domain partitioning.
The effort involved extensive stakeholder discussions, DDD modeling, and three months of intensive development, testing, and iteration, ultimately delivering a stable platform for the conference.
Large‑Scale Promotion Preparation (618 & 11.11)
The team adopts a repeatable preparation cycle: review past events, identify bottlenecks, optimize critical paths, conduct stress tests, and perform post‑mortem analysis. Continuous improvement of monitoring, fallback mechanisms, and automated degradation safeguards ensures resilience during traffic spikes.
AI Assistant Development
Following the rise of ChatGPT in late 2022, the author’s group built a merchant AI assistant to address information overload, low‑quality chatbot responses, and fragmented workflows. Starting from zero, they formed a large‑model R&D team, created a modular platform, and iterated through three quarters to reach version 3.0 with gray‑release, permission control, rate limiting, and full‑stack observability.
Continuous Learning and Mindset
The author stresses the importance of humility, deep technical exploration (e.g., reading Tomcat source), and a disciplined learning routine—practice, think, and study. Embracing challenging tasks, such as mastering HTTP in real‑world scenarios, helps sustain growth beyond the early career stage.
Signed-in readers can open the original source through BestHub's protected redirect.
This article has been distilled and summarized from source material, then republished for learning and reference. If you believe it infringes your rights, please contactand we will review it promptly.
JD Retail Technology
Official platform of JD Retail Technology, delivering insightful R&D news and a deep look into the lives and work of technologists.
How this landed with the community
Was this worth your time?
0 Comments
Thoughtful readers leave field notes, pushback, and hard-won operational detail here.
