.. references, and analyst data. Handle the end-to-end sales process through engagement of appropriate resources such as Pre-Sales Consultants, Business Development Consultants, Oracle Consulting, Executives and Partners etc., and through effective utilization of selling tools such as Engineered selling .. read more
Job Title: Senior Data Engineer Location: Gurgaon (Hybrid) Experience Required: 6 to 9 years
Job Summary: We are seeking a highly skilled Senior Data Engineer with expertise in PySpark, Python, ETL processes, and data pipeline development. The ideal candidate will have a strong background in Snowflake and hands-on experience working with AWS cloud. You will be responsible for designing, developing, and optimizing scalable data pipelines and ETL processes, ensuring efficient data flow and storage.
Key Responsibilities: - Design, develop, and maintain large-scale, complex data pipelines using PySpark, Python, and other relevant tools. - Collaborate with cross-functional teams to understand data requirements and architect efficient data models and flows. - Implement robust ETL processes to gather, transform, and load data from various sources into Snowflake, ensuring data accuracy, consistency, and reliability. - Leverage AWS cloud services such as S3, Lambda, Redshift, Glue, and EMR to build and optimize data storage and processing pipelines. - Optimize performance of data workflows, ensuring scalability, fault tolerance, and low-latency data processing. - Collaborate with Data Analysts, Data Scientists, and other stakeholders to deliver data-driven insights and analytics. - Monitor, troubleshoot, and improve existing data pipelines for better performance and reliability. - Ensure best practices are followed in terms of data security, data governance, and compliance with AWS services. - Provide mentorship to junior team members and contribute to team knowledge sharing.
Qualifications: - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. - 6-9 years of experience in data engineering, with a strong focus on PySpark, Python, and Snowflake. - Hands-on experience with AWS Cloud technologies (e.g., S3, Redshift, Lambda, Glue, EMR). - Proven expertise in designing and optimizing complex ETL processes and data pipelines. - Solid understanding of data warehousing concepts, data architecture, and performance tuning. - Experience with Snowflake for data storage, querying, and performance optimization. - Strong proficiency in SQL and experience with database systems (e.g., PostgreSQL, MySQL, Redshift). - Familiarity with DevOps practices, CI/CD pipelines, and version control (e.g., Git, Jenkins). - Excellent problem-solving skills, with a keen eye for detail and a focus on delivering high-quality work. - Ability to work independently and as part of a team in a fast-paced, dynamic environment.
Job Summary: We are seeking a highly skilled Senior Data Engineer with expertise in PySpark, Python, ETL processes, and data pipeline development. The ideal candidate will have a strong background in Snowflake and hands-on experience working with AWS cloud. You will be responsible for designing, developing, and optimizing scalable data pipelines and ETL processes, ensuring efficient data flow and storage.
Key Responsibilities: - Design, develop, and maintain large-scale, complex data pipelines using PySpark, Python, and other relevant tools. - Collaborate with cross-functional teams to understand data requirements and architect efficient data models and flows. - Implement robust ETL processes to gather, transform, and load data from various sources into Snowflake, ensuring data accuracy, consistency, and reliability. - Leverage AWS cloud services such as S3, Lambda, Redshift, Glue, and EMR to build and optimize data storage and processing pipelines. - Optimize performance of data workflows, ensuring scalability, fault tolerance, and low-latency data processing. - Collaborate with Data Analysts, Data Scientists, and other stakeholders to deliver data-driven insights and analytics. - Monitor, troubleshoot, and improve existing data pipelines for better performance and reliability. - Ensure best practices are followed in terms of data security, data governance, and compliance with AWS services. - Provide mentorship to junior team members and contribute to team knowledge sharing.
Qualifications: - Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. - 6-9 years of experience in data engineering, with a strong focus on PySpark, Python, and Snowflake. - Hands-on experience with AWS Cloud technologies (e.g., S3, Redshift, Lambda, Glue, EMR). - Proven expertise in designing and optimizing complex ETL processes and data pipelines. - Solid understanding of data warehousing concepts, data architecture, and performance tuning. - Experience with Snowflake for data storage, querying, and performance optimization. - Strong proficiency in SQL and experience with database systems (e.g., PostgreSQL, MySQL, Redshift). - Familiarity with DevOps practices, CI/CD pipelines, and version control (e.g., Git, Jenkins). - Excellent problem-solving skills, with a keen eye for detail and a focus on delivering high-quality work. - Ability to work independently and as part of a team in a fast-paced, dynamic environment.