Senior Magento Backend Developer

Role: Magento Backend Developer

Exp- 5-8 Years

Location: Bangalore, Whitefield

Communication: Excellent english communication skills

Notice period: Immediate Joiners

The Senior Back-End Developer (eCommerce) will design, build, test, debug, document, support, and enhance application software that enables the enterprise eCommerce platform. 

Required Experience and Skills:

- Bachelor's degree (or equivalent work experience) in information technology

- Outstanding written and verbal communication skills

- A minimum of 5 years developing and supporting solutions for a commercial enterprise using the technologies listed below

o Magento 2.x (either open-source or Adobe)

o PHP

o GraphQL

o MySQL or SQL Server

o API development and implementation

Responsibilities:

- Understand the data, components, integrations, and runtime environment that comprise the eCommerce platform

- Collaborate with Digital Product Owners to understand, clarify, and enrich business and technical requirements, specifications, and acceptance criteria

- Maintain and demonstrate a working knowledge of the team's standards and best practices

- Partner with architects, technical leaders, and/or peers to identify optimal solutions, mitigate risks, and leverage opportunities accordingly

- Work with Quality Assurance (QA) professionals to craft test cases and supporting data

- Assist in the estimation of overall work effort, calendar duration, and dependencies associated with a deliverable

- Engineer applications, integrations, and data pipelines with well-structured, scalable, efficient, fault-tolerant, code that would serve as a standard of excellence for other developers

- Build and/or enhance automated unit tests to ensure code quality, resilience, and high code coverage

- Follow established best practices for code management, information security, and release management

- Maintain clear, professional, open communication across all levels of the team

- Actively participate in and contribute to appropriate agile ceremonies, peer reviews, and knowledge sharing

- Author and maintain documentation including but not limited to contextual diagrams, technical specifications, procedures, configuration settings, and dependencies

- Provide timely and responsive triage, troubleshooting, and resolution of issues in all relevant environments

Preferred Experience and Skills:

- Adobe Commerce / Experience Cloud (or equivalent commerce platform)

- Github (or equivalent code management)

- Jira (or equivalent work management)

- Confluence (or equivalent knowledge management)

- MuleSoft and/or Azure API Management

- Databricks

- Azure Data Lake

Questionnaire:-

-

You are managing a Magento 2.x (Adobe Commerce) eCommerce platform handling a
product catalog of 10 to 25 million SKUs

(Stock Keeping Units).

Recently, the business team has reported
performance degradation

on category pages and slow response times during promotional campaigns.

How would you approach identifying and resolving these issues?

Performance Optimization:

• How you would analyze bottlenecks in Magento (e.g., database queries, PHP code, indexing, API latency, etc.)

• Techniques or tools you would use for profiling and optimizing Magento performance at scale.

Indexer Issues:

• Common indexer-related challenges in large catalogs and how you would optimize or schedule reindexing to avoid downtime or locking issues.

Cache Management:

• How Magento's caching (Full Page Cache, Redis, Varnish) can be tuned for high-performance setups, especially under promotional traffic spikes.

Promotional Catalog Rules:

• How catalog and cart price rules impact performance and how to manage or optimize them for large data sets.

Scalability:

• Best practices for managing Magento when product size grows from 10 million to 25 million SKUs, including database sharding, asynchronous processing, or using external systems for search and indexing (like Elasticsearch or OpenSearch).

Code and API Optimization:

• How to ensure GraphQL or REST APIs perform efficiently with such large product data sets.