Data Science engineer

Title : DATA SCIENCE ENGINEER (remote)

Position Overview :

The DATA SCIENCE ENGINEER in the Production Support Team plays a crucial role in ensuring the reliability, performance, and continuous optimization of data-driven systems across both cloud and on-prem environments.

This role combines data analysis, system troubleshooting, and technical support to resolve complex issues, enhance system efficiency, and support business-critical operations.

The Data Scientist will work closely with cross-functional teams to monitor, analyse, 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 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.

Key Qualifications :

Bachelor s degree in Computer Science, Information Technology, Engineering, or a related field.

4 years of proven experience working with on-prem and cloud-based systems (Snowflake, Teradata, Hadoop, SQL Server).

Experience in data pipeline management, ETL processes, and managing data-related challenges. " Familiarity with development frameworks (e.g., .NET, Java) for handling urgent small development tasks.

Expertise in Spark, Snowflake, Teradata, SQL Server, Hadoop, and other big data technologies.

Strong understanding of database manage