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From:
mohit,
Spar
mohit.n@sparinfosys.com
Reply to:   mohit.n@sparinfosys.com
 

 

Title: Data  Engineer with Snowflake

Location is Remote

Long Term

Best Rate: $53/hr on c2c

JD:

 

Data Science Engineer with Snowflake and Production Support Experience

 

Key Responsibilities:

 

Data Analysis & Insights Generation:

• Analyze and interpret large datasets from both on-premise and cloud environments (Snowflake, Teradata, SQL Server) to extract meaningful insights for system optimization and business decisions.

• Work with stakeholders to provide actionable recommendations based on data analysis results.

Production System Monitoring & Maintenance:

• Continuously monitor the performance, stability, and data integrity of production systems in both cloud (Snowflake, Kafka) and on-premise environments (SQL Server, Teradata, Hadoop).

• Troubleshoot and resolve system performance issues, data discrepancies, and application errors to ensure seamless operations.

 

ETL & Data Pipeline Management:

• Develop, maintain, and optimize ETL processes using Spark, Hadoop, and other big data technologies to ensure efficient and timely data movement across platforms.

• Implement and enhance data processing workflows to support complex data transformations and integrations across multiple systems.

 

Application & Service Support:

• Provide production support for enterprise applications including WebSphere, PEGA, and Kafka, ensuring minimal downtime and rapid resolution of service disruptions.

• Collaborate with development teams to resolve issues in application stacks such as .NET, Java, and Angular, maintaining system stability and performance.

 

Performance Optimization & Query Tuning:

• Optimize queries and improve performance for large-scale data processing in Teradata, Snowflake, and SQL Server.

• Enhance the efficiency of distributed data tasks and computation within Spark and Hadoop environments.

 

Data Integration & Automation:

• Manage and automate data integration tasks between different environments (on-premise and cloud) using tools like Kafka and FTP.

• Ensure smooth data transfers, monitor batch jobs, and implement automation for data processing and system alerts.

 

Security & Compliance:

• Ensure data handling, transfer protocols, and storage meet organizational security standards and compliance regulations (including the use of FTP and secure communication).

• Apply best practices in data governance and privacy in both cloud and on-prem environments.

 

Documentation & Reporting:

• Document data processes, system configurations, and troubleshooting steps to create a knowledge repository.

• Provide detailed reports on system performance, issue resolution, and recommendations for future enhancements.

 

Collaboration & Stakeholder Communication:

• Work closely with cross-functional teams, including DevOps, engineering, and business analysts, to ensure data solutions align with overall system requirements.

• Communicate technical findings clearly to non-technical stakeholders to support informed decision-making.