Manager-Data Engineering-Cloud Data Engineering

3 days ago

Gurgaon, Haryana, India EXL Full time ₹ 12,00,000 - ₹ 36,00,000 per year
Description
  • Design, develop, and maintain scalable and efficient data pipelines using PySpark and Databricks
  • Perform data extraction, transformation, and loading (ETL) from diverse structured and unstructured data sources
  • Develop and maintain data models, data warehouses, and data marts in Databricks
  • Proficient in Python, Apache Spark, and PySpark 
  • Integrate third party data from multiple sources with internal data 
  • Write and optimize complex SQL queries for high performance and scalability across large datasets
  • Collaborate closely with data scientists, analysts, and business stakeholders to gather and understand data requirements.
  • Ensure data quality, consistency, and integrity throughout the data lifecycle using validation and monitoring techniques.
  • Develop and maintain modular, reusable, and well-documented code and technical documentation for data workflows and processes.
  • Implement data governance, security, and compliance best practices.
Responsibilities
  • Strong hands-on experience with PySpark, Databricks, and distributed data processing.
  • Proficiency in Python, with deep knowledge of Pandas and NumPy for data manipulation and analysis.
  • Strong foundation in Data Structures and Algorithms for writing efficient and optimized code.
  • Solid understanding of ETL processes, data modeling, and data warehousing concepts.
  • Experience with SQL and performance tuning for large-scale datasets.
  • Familiarity with data visualization tools such as Power BI, or Tableau.
  • Knowledge of version control systems like Git and collaborative development workflows.
  • Strong problem-solving skills, attention to detail, and ability to work independently or in a team.
Qualifications
  • 5+ years of relevant experience in Data Engineering tools
  • Programming Languages: Python and SQL
  • Data Processing Tools: pandas, NumPy, PySpark
  • Cloud Platforms: Databricks (for scalable computing resources)
  • Version Control & Collaboration: Git, GitHub, GitLab
  • Deployment and Monitoring: Databricks, Github Actions

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