Online fashion, beauty and home lifestyle marketplace for India
Myntra runs a large-scale fashion and lifestyle commerce platform on a JVM-and-Go backend (Java, Spring, Scala, Go) with Kafka, Redis, Aerospike and Solr, plus a Spark, Databricks and Ray data layer. Hiring leans product- and ops-heavy rather than engineering-led, and pain points cluster around category management: cataloging, demand forecasting, availability and return-to-origin. That points to a focus on merchandising efficiency over new infrastructure.
Notable leadership hires: Technical Lead, FP&A Director, Deputy Director
Myntra is a Bengaluru-headquartered online lifestyle platform founded in 2007, selling fashion, beauty and home decor to consumers across India. The company is privately held and employs between 1,001 and 5,000 people. Its offerings include personalization features such as an AI stylist, a fast-delivery service, and creator-led and live commerce formats. Teams span product, engineering, design, marketing, data science, category management and operations, with hiring concentrated in India.
Myntra uses Java, Spring, Scala, Go and Python with Kafka, Redis, Aerospike, MySQL, MongoDB and Solr, plus Apache Spark, Databricks, Ray, Airflow and Dagster for data, and Oracle Fusion, SAP and Workday for back office.
Yes. Hiring is accelerating, led by product and operations roles, with engineering, marketing, sales, data, finance and design also recruiting. Openings are in India and skew senior, including director and principal levels.
Job posts point to category visibility planning, vendor analytics reconciliation, Oracle Fusion workflow optimization, international brand acquisition, scaling brands on the platform and an internal developer portal.
Other companies in the same industry, closest in size
Myntra's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.