Data Engineer

4 days ago

Ghaziabad, Uttar Pradesh, India etelligens Technologies Full time ₹ 20,00,000 - ₹ 25,00,000 per year

About The Role
We are seeking an experienced Data Engineer - Python with 58 years of hands-on expertise in building scalable data solutions. The ideal candidate will design, develop, and optimize ETL pipelines, ensure data quality and reliability, and collaborate with cross-functional teams to enable data-driven decision-making.

Key Responsibilities

  • Design, develop, and maintain ETL/ELT pipelines using Python, PySpark, and SQL.
  • Build and optimize scalable data pipelines leveraging AWS services (Glue, Lambda, S3, Athena, Step Functions).
  • Implement and manage Data Warehousing solutions with strong knowledge of SCD (Type 1, Type 2) and Medallion Architecture.
  • Develop efficient data models and ensure partitioning, indexing, and performance optimization in big data environments.
  • Ensure high standards of data quality, governance, and security across pipelines and platforms.
  • Collaborate with data scientists, analysts, and business stakeholders to translate business requirements into technical solutions.
  • Monitor and troubleshoot production pipelines to ensure reliability and scalability.
  • Contribute to automation, process improvements, and documentation for data engineering workflows.

Required Skillsets

  • 5-8 years of proven experience in Data Engineering.
  • Strong proficiency in Python, PySpark, and SQL for data processing and Solid understanding of ETL/ELT design principles and experience with AWS services (Glue, Lambda, S3, Athena, Step Functions).
  • Hands-on experience with Data Warehousing concepts, SCD (Type 1, Type 2), and Medallion Architecture.
  • Expertise in data modeling, partitioning strategies, and query performance tuning.
  • Strong problem-solving and debugging skills in big data environments.
  • Excellent communication skills to explain technical concepts to non-technical stakeholders.

Nice-to-Have Skills

  • Experience with DBT for data transformation and testing.
  • Exposure to Databricks and the Lakehouse architecture.
  • Familiarity with CI/CD for data pipelines and infrastructure-as-code (Terraform, Knowledge of data security, compliance, and governance best practices.

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