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Python Specialist Data Engineer

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Job Description – Python Specialist Data Engineer

Job Title: Python Specialist Data Engineer
Experience: 5–8 Years
Employment Type: Full-Time
Location: Open / As per project requirement

Role Overview

We are seeking an experienced Python Specialist Data Engineer with strong hands-on expertise in Azure Data Services. The role focuses on building scalable data pipelines, cloud-based data solutions, and supporting analytics and business intelligence initiatives.

Key Responsibilities

  • Develop, optimize, and maintain scalable data pipelines using Python
  • Design end-to-end data solutions using Azure Synapse Analytics and Azure Data Lake
  • Build data models, transformations, and workflows for analytics and reporting
  • Implement CI/CD pipelines and deployment automation using Azure DevOps
  • Work with MS SQL Server, T-SQL, and PL/SQL for data warehousing solutions
  • Ensure best practices for data ingestion, transformation, quality, and security
  • Collaborate with data scientists, analysts, and business stakeholders
  • Monitor performance and troubleshoot data platform issues

Required Skills & Qualifications

  • Strong proficiency in Python for ETL/ELT and data processing
  • Hands-on experience with Azure Synapse, Data Lake (Gen2), Logic Apps, Azure DevOps
  • Solid understanding of data warehouse concepts (star/snowflake schema)
  • Advanced skills in SQL and PL/SQL
  • Experience integrating structured and unstructured data sources
  • Knowledge of cloud data security, governance, and compliance

Preferred Qualifications

  • Azure certification (DP-203 or equivalent)
  • Experience with Git and version control
  • Knowledge of Azure Data Factory
  • Exposure to Agile/Scrum methodologies

Job Types: Full-time, Permanent

Pay: ₹1,474,883.14 - ₹2,820,204.87 per year

Benefits:

  • Health insurance
  • Provident Fund

Application Question(s):

  • Do you have hands-on experience with Python for ETL/ELT and data pipeline development?
  • Which Azure Data Services have you worked on?

Work Location: In person

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