成為 Converse 團隊的一員

Converse 是一個探索潛力、打破藩籬、超越可能的天地。公司想尋找能夠成長、思考、創造、懷抱夢想的人才, 並擁抱多元性,鼓勵大家發揮想像力,讓企業文化欣欣向榮。品牌想尋找能達成目標者、管理者及有遠見的夢想家。Converse 希望每個人都能在挑戰性十足且不斷演變的環境中,揮灑熱情並一展所長,以團隊合作締造美好成果。


WHO YOU’LL WORK WITH


This role typically reports to a Senior Manager, Data Engineering within Global Converse at the India Technology Center (ITC). You will partner with Supply Chain & Logistics, Product, Engineering, Data Platform, Planning, Merchandising, Finance, and business teams to deliver trusted data products, scalable analytics, and decision-ready insights.

 

WHO WE ARE LOOKING FOR


We are looking for a hands-on Senior Data Engineer who combines strong data engineering capabilities with practical analytics and business intelligence experience. You will design reliable data pipelines and curated datasets using Snowflake or Databricks, apply strong SQL and data modeling skills, and enable decision-ready reporting through Tableau or Sigma. Experience supporting Supply Chain or Logistics is preferred, as this role will translate operational questions across inventory, order flow, fulfillment, transportation, and delivery performance into trusted data solutions. You should be comfortable working across the full data lifecycle, from understanding business requirements and designing scalable models to production support, performance optimization, data quality, and analytics adoption. The ideal candidate communicates clearly, collaborates across global teams, and takes ownership from problem definition through delivery and continuous improvement.

  • 6-8 years of relevant experience in data engineering, analytics engineering, business intelligence, data analytics, or related roles.
  • Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Analytics, or an equivalent combination of education, experience, and training.
  • Strong SQL skills and hands-on data engineering experience with Snowflake or Databricks; experience with both is preferred.
  • Experience using Tableau or Sigma to deliver dashboards, governed KPIs, semantic models, and self-service analytics.
  • Preferred: experience delivering data or analytics solutions for Supply Chain, Logistics, inventory, order management, fulfillment, transportation, or related retail operations.

 

WHAT YOU’LL WORK ON


You will build and support scalable data products that help Supply Chain & Logistics and other enterprise partners make faster, better-informed decisions. You will combine data engineering, analytical modeling, and BI skills to turn business questions into reliable, production-ready datasets and analytics solutions.

  • Partner with Supply Chain & Logistics stakeholders to understand operational questions, define measures, and translate requirements into scalable data solutions.
  • Build, test, deploy, and maintain data pipelines, transformations, and curated analytical datasets using SQL, Snowflake, Databricks, Python, Spark, or related technologies.
  • Design reusable dimensional data models and semantic layers that improve consistency, quality, discoverability, and trust across reporting and analytics.
  • Develop and evolve Tableau or Sigma dashboards, KPI frameworks, and self-service analytics products that enable users to monitor performance, investigate drivers, and take action.
  • Analyze and enable insights across demand, inventory, order fulfillment, transportation, delivery performance, and related operational measures.
  • Collaborate with Product Managers, Engineers, Platform teams, and business partners through design reviews, testing, release, adoption, and production support.
  • Improve engineering quality through automated testing, code reviews, documentation, performance tuning, observability, and reliable DevOps practices.

重要須知

招募策略

01 申請

我們的團隊由多元技能、知識庫、意見、想法和背景組成。 我們希望你找到合適的職位:查看職務說明、部門和團隊,探索適合你的角色。

02 與招募人員會面或進行評估

若獲選擔任公司職務,招募人員會與你聯絡,以展開面試流程,並在整個流程中擔任你的主要聯絡人。 若為 Retail 職務,你將完成包含對談和測驗的互動式評估,完成評估約需 10 至 20 分鐘的時間。 無論是哪個角色,我們都想瞭解你的各種面向,因此請不要避談你如何提供世界級的服務,以及你的與眾不同之處。

03 面試

在進入這個階段時,可先做好研究,瞭解我們在尋找的人才,並為深入瞭解你及相關背景而設定的問題做好準備,自信應對。

兩個人在戶外微笑擁抱