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LTIMindtree

Data Modeller

LTIMindtree

Coimbatore, Tamil Nadu, India ・ フルタイム

最初に応募しよう

経験
5–12 yrs
給料
求人情報
1
投稿済み
5時間前

Where you'll work

仕事内容

Role Overview

LTIMindtree is hiring an experienced Data Modeller to work from Coimbatore or Bengaluru. The role is focused on designing enterprise-level data models and supporting data engineering activities for the insurance domain.

Responsibilities

  • Develop robust enterprise data models with a strong emphasis on insurance use cases.
  • Convert and map data from source platforms, including Eclipse, into target data lake setups.
  • Use Azure Databricks and Azure Data Factory to ingest, shape, and transform data.
  • Create and improve data engineering workflows and transformations in Azure Databricks.
  • Prepare conceptual, logical, and physical data models, and produce master and subject-area ER diagrams in ERwin Data Modeler 14.0.
  • Reverse-engineer database schemas through Databricks API connection files for ERwin.
  • Diagnose issues and design, build, and deploy cloud-based integration solutions using Databricks.
  • Work closely with customers and internal stakeholders to understand requirements and deliver suitable data solutions.

Requirements

  • 5 to 12 years of experience in data modelling and related delivery.
  • Strong background in data modelling and data mapping.
  • Practical experience with Azure Databricks and Azure Data Factory.
  • Ability to create CDM, LDM, PDM, and ER diagrams using ERwin Data Modeler.
  • Understanding of data lake architecture and cloud integration patterns.
  • Strong communication skills with customer-facing and stakeholder management experience.
  • Hands-on experience in the insurance domain.
  • Exposure to the reinsurance domain is considered a plus.

Additional Information

Location: Coimbatore / Bengaluru

This opportunity is being shared from a third-party job source.

スキル

コミュニケーションスキル ステークホルダーマネジメント Data Modeling Insurance Domain Knowledge Data Mapping Azure Databricks Azure Data Factory ERwin Data Modeler Data Lake Architecture Cloud Integration Schema Reverse Engineering

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