Job Summary
The Data Scientist in the Production Support Team plays a crucial role in ensuring the reliability, performance, and continuous optimization of data-driven systems in both cloud and on-prem environments. This role combines data analysis, system troubleshooting, and technical support to resolve complex issues, improve system efficiency, and support business-critical operations. The Data Scientist will collaborate closely with cross-functional teams to monitor, analyze, and address data processing, integration, and performance challenges, ensuring seamless operation of applications and services used by clients.
Key Responsibilities
Data Analysis & Insights Generation :
- Analyze large datasets from both on-premise and cloud environments (Snowflake, Teradata, SQL Server) to extract insights that drive system optimization and support 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 cloud (Snowflake, Kafka) and on-premise environments (SQL Server, Teradata, Hadoop).Troubleshoot and resolve system performance issues, data discrepancies, and application errors to ensure smooth operations.ETL & Data Pipeline Management :
Develop, maintain, and optimize ETL processes using Spark, Hadoop, and other big data technologies to ensure timely data movement across platforms.Implement and enhance data processing workflows to support complex transformations and integrations across multiple systems.Application & Service Support :
Provide production support for enterprise applications including WebSphere, PEGA, and Kafka, ensuring minimal downtime and quick 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 distributed data tasks and computation efficiency within Spark and Hadoop environments.Data Integration & Automation :
Manage and automate data integration tasks between on-premise and cloud environments using tools like Kafka and FTP.Monitor batch jobs, implement automation for data processing, and set up system alerts for timely resolution.Security & Compliance :
Ensure that data handling, transfer protocols, and storage comply with organizational security standards and regulatory requirements (e.g., 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 maintain a comprehensive knowledge repository.Provide detailed reports on system performance, issue resolution, and recommendations for future improvements.Collaboration & Stakeholder Communication :
Collaborate with cross-functional teams, including DevOps, engineering, and business analysts, to ensure data solutions align with system requirements.Effectively communicate technical findings to non-technical stakeholders to support informed decision-makingRequired Qualifications
Education : Bachelor's degree in computer science, Information Technology, Engineering, or a related field.
Experience :
4 years of experience working with on-premises and cloud-based systems (e.g., Snowflake, Teradata, Hadoop, SQL Server).Experience in data pipeline management, ETL processes, and managing data-related challenges.Technical Skills :
Expertise in Spark, Snowflake, Teradata, SQL Server, Hadoop, and other big data technologies.Strong understanding of database management and performance tuning.Familiarity with development frameworks (e.g., .NET, Java) for handling urgent development tasks.Preferred Qualifications
Experience with cloud-based data platforms such as Snowflake and Kafka.Hands-on experience with Spark, Hadoop, and data pipeline management.Familiarity with data governance and security compliance in cloud and on-prem environments.Certifications
Certifications in cloud technologies (e.g., Snowflake, AWS, Azure) or big data platforms (e.g., Spark, Hadoop) are a plus but not required.
Education : Bachelors Degree
