Data And Integration Engineer

Key Responsibilities

• Design, develop, and maintain scalable data pipelines, ETL/ELT workflows, and data integration solutions.
• Develop and optimize complex SQL queries, views, stored procedures, and data models for analytics and operational reporting.
• Integrate data from REST APIs, Graph APIs, enterprise applications, databases, and cloud platforms.
• Process, transform, and manage structured, semi-structured, and unstructured data, including JSON.
• Design, implement, and support cloud-based data solutions on Azure or AWS.
• Troubleshoot and resolve data quality, synchronization, transformation, and integration issues.
• Collaborate with business and technical stakeholders to gather data requirements and deliver scalable integration solutions.
• Maintain technical documentation, data mappings, and integration workflows while adhering to data governance best practices.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or a related field.
• 7+ years of experience in Data Engineering, Data Integration, or ETL development.
• Strong expertise in SQL, including joins, views, stored procedures, query optimization, and performance tuning.
• Hands-on experience with REST APIs, Microsoft Graph APIs, and enterprise application integrations.
• Experience working with JSON, XML, and data transformation techniques.
• Strong understanding of data modeling, normalization, and relational database concepts.
• Experience with Azure services (Storage, Azure SQL, Logic Apps) or AWS services (Redshift, S3, Lambda, Glue).
• Experience with Microsoft Power Automate or Azure Logic Apps for workflow automation.
• Proficiency with Git, Visual Studio Code, and version control best practices.
• Strong analytical, troubleshooting, and problem-solving skills.

Preferred Skills

• Experience integrating with enterprise platforms such as ServiceNow, Dynatrace, SolarWinds, and Nexthink.
• Programming experience using Python for data processing and automation.
• Knowledge of data architecture, metadata management, and data governance principles.
• Experience supporting AI, automation, and cloud-native data solutions.
• Familiarity with Agile/Scrum development methodologies and DevOps practices.