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Senior Data Engineer, Global Converse, ITC

Posted Yesterday
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Remote
Hiring Remotely in Karnataka, IND
Senior level
Remote
Hiring Remotely in Karnataka, IND
Senior level
Build and maintain scalable data pipelines, curated datasets, dimensional models, semantic layers, and BI solutions for supply chain and logistics analytics. Translate business requirements into trusted data products using SQL, Snowflake, Databricks, Python, and Spark. Develop Tableau or Sigma dashboards, improve data quality and performance, support production systems, and collaborate with product, engineering, platform, and business teams to drive analytics adoption and operational decision-making.
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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.

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