Python Data Engineer

Key Responsibilities - Design, implement, and maintain scalable data pipelines and architectures for data processing and storage. - Collaborate with data scientists and analysts to understand data requirements and deliver solutions that meet business needs. - Optimize and improve data workflows for efficiency, including the automation of data integration processes. - Ensure data quality and integrity through rigorous testing and validation procedures. - Develop and maintain documentation for data engineering processes and systems. - Monitor system performance and troubleshoot data-related issues as they arise. - Stay updated with the latest industry trends and integrate new technologies into existing data systems. - Lead and mentor junior data engineers, providing guidance and support in their professional development.

Overview - The Senior Python Data Engineer will play a vital role in advancing our data engineering capabilities within the Information Technology Enabled Services (ITES) sector. - Based in either Bangalore or Hyderabad, this position involves working on complex data systems to support business intelligence and analytics. - As a Level 3 Engineer with 3-6 years of experience, you will be responsible for designing, developing, and optimizing scalable data pipelines and architecture. - This role requires a deep understanding of data processing and Python programming to transform raw data into actionable insights. - You will collaborate with cross-functional teams to integrate data from various sources, ensuring data consistency and accuracy. - Your contributions will directly impact data-driven decision-making processes across the organization. - The position offers the opportunity to work with cutting-edge technologies and tools, fostering both personal and professional growth in a dynamic tech environment.

Requirements - Proven experience as a Data Engineer with 4-6 years of relevant work in Python programming and data processing. - Strong proficiency in Python and experience with libraries such as Pandas, NumPy, and PySpark. - Experience with SQL and NoSQL databases for data storage and querying. - Familiarity with data warehousing solutions like Amazon Redshift or Google BigQuery. - Understanding of ETL (Extract, Transform, Load) processes and data integration techniques. - Knowledge of cloud platforms such as AWS, Azure, or Google Cloud for data solutions. - Excellent problem-solving skills and ability to work collaboratively in a team environment.