Data Engineer

Data Engineering

• Design, build, and maintain data pipelines on-premises and in the cloud (Azure, GCP, AWS) to ingest, transform, and store large datasets. Ensure pipelines are reliable and support multiple business use cases.
• Create and optimize dimensional models (star / snowflake) to improve query performance and reporting. Ensure models are consistent, scalable, and easy for analysts to use.
• Integrate data from SQL, NoSQL, APIs, and files while maintaining accuracy and completeness. Apply validation checks and monitoring to ensure high‑quality data.
• Improve ETL / ELT processes for efficiency and scalability. Redesign workflows to remove bottlenecks and handle large, disconnected datasets.
• Build and maintain end-to-end ETL / ELT pipelines with SSIS and Azure Data Factory. Implement error handling, logging, and scheduling for dependable operations.
• Automate deployment, testing, and monitoring of ETL workflows through CI / CD pipelines. Integrate releases into regular deployment cycles for faster, safer updates.
• Manage data lakes and warehouses with proper governance. Apply security best practices, including access controls and encryption.
• Partner with engineers, analysts, and stakeholders to translate requirements into solutions. Prepare curated data marts and fact / dimension tables to support self‑service analytics.

Data Analytics

Qualifications

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