Big Data Engineer - Python

Qcentrio

Bengaluru

On-site

INR 4,000,000 - 6,000,000

Full time

14 days+

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Job summary

Qcentrio is seeking an experienced Data Engineer with 5+ years of hands-on work building scalable data infrastructure. You will design and develop robust ETL pipelines and manage large-scale datasets to support critical business functions.

This role requires deep technical expertise, problem-solving skills, and the ability to thrive in a fast-paced environment. Key responsibilities include designing ETL/ELT pipelines, modeling data for performance, and leveraging Spark/Hadoop in cloud

Qualifications

  • 7–10 years of relevant experience in bigdata engineering.
  • Advanced proficiency in Python.
  • Strong SQL skills for complex data manipulation and analysis.
  • Hands-on experience with Apache Spark and Hadoop.
  • Cloud development experience with Azure/AWS/GCP.
  • Knowledge of data lake and data lakehouse architectures.
  • ETL performance tuning and cost optimization expertise.
  • Familiarity with modern software engineering practices.

Responsibilities

  • Design, develop, and maintain scalable ETL/ELT pipelines for large data volumes.
  • Model and structure data for performance and scalability.
  • Work with cloud infrastructure (Azure preferred) to optimize workflows.
  • Leverage Spark/Hadoop for large-scale processing.
  • Build and manage data lake/lakehouse architectures.
  • Optimize ETL performance and cost-efficiency.
  • Collaborate with data science, analytics, and software engineering teams.
  • Ensure data quality, integrity, and security throughout the lifecycle.

Tools

Apache Airflow
Azure Data Factory

Job description

We are seeking an experienced and driven Data Engineer with 5+ years of hands-on experience in building scalable data infrastructure and systems. You will play a key role in designing and developing robust, high-performance ETL pipelines and managing large-scale datasets to support critical business functions. This role requires deep technical expertise, strong problem-solving skills, and the ability to thrive in a fast-paced, evolving environment.

Key Responsibilities
  • Design, develop, and maintain scalable and reliable ETL/ELT pipelines for processing large volumes of data (terabytes and beyond).
  • Model and structure data for performance, scalability, and usability.
  • Work with cloud infrastructure (preferably Azure) to build and optimize data workflows.
  • Leverage distributed computing frameworks like Apache Spark and Hadoop for large-scale data processing.
  • Build and manage data lake/lakehouse architectures in alignment with best practices.
  • Optimize ETL performance and manage cost-effective data operations.
  • Collaborate closely with cross-functional teams including data science, analytics, and software engineering.
  • Ensure data quality, integrity, and security across all stages of the data lifecycle.
Required Skills & Qualifications
  • 7 to 10 years of relevant experience in bigdata engineering.
  • Advanced proficiency in Python,
  • Strong skills in SQL for complex data manipulation and analysis.
  • Hands-on experience with Apache Spark, Hadoop, or similar distributed systems.
  • Proven track record of handling large-scale datasets (TBs) in production environments.
  • Cloud development experience with Azure (preferred), AWS, or GCP.
  • Solid understanding of data lake and data lakehouse architectures.
  • Expertise in ETL performance tuning and cost optimization techniques.
  • Knowledge of data structures, algorithms, and modern software engineering practices.
Soft Skills
  • Strong communication skills with the ability to explain complex technical concepts clearly and concisely.
  • Self-starter who learns quickly and takes ownership.
  • High attention to detail with a strong sense of data quality and reliability.
  • Comfortable working in an agile, fast-changing environment with incomplete requirements.
Preferred Qualifications
  • Experience with tools like Apache Airflow, Azure Data Factory, or similar.
  • Familiarity with CI/CD and DevOps in the context of data engineering.
  • Knowledge of data governance, cataloging, and access control principles.
Skills

Python,Sql,Aws,Azure, Hadoop

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