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We are seeking a Senior Data Engineer (G10) with strong expertise in ETL development, Microsoft Azure, and SSIS, to join as an individual contributor. This role focuses on designing, developing, and maintaining robust data pipelines and platforms, while also taking ownership of the data environment, ensuring stability, performance, and scalability. The ideal candidate is a hands-on engineer with deep ETL experience, capable of working independently and ensuring the smooth operation of data systems in production.
Job Responsibility
Design, develop, and maintain ETL/ELT pipelines using SSIS, Azure Data Factory, and related tools
Build and optimize data integration workflows across multiple data sources
Develop and maintain data models and transformations to support analytics and reporting
Write and optimize complex SQL queries for large datasets
Work with Microsoft Azure services (Data Factory, Azure SQL, storage, etc.) to build scalable data solutions
Support migration and modernization of on-premise ETL solutions (SSIS) to Azure where applicable
Utilize Azure Databricks (nice to have) for advanced data processing
Take ownership of the data platform environment, ensuring availability, performance, and reliability
Monitor, troubleshoot, and resolve data pipeline and system issues in production
Implement and maintain scheduling, logging, and alerting mechanisms
Ensure data quality, consistency, and integrity across systems
Apply data governance, security, and compliance standards
Follow and promote best practices in ETL development, version control, and deployment
Contribute to documentation and standardization of data processes
Work closely with data architects, analysts, and business stakeholders to deliver data solutions
Support integration of data systems with downstream applications and reporting tools
Requirements
7–10 years of experience in data engineering and ETL development
Strong hands-on experience with SSIS
Proven experience with Microsoft Azure (Data Factory, Azure SQL, storage services)
Strong expertise in SQL (performance tuning, optimization)
Experience building and maintaining end-to-end ETL pipelines
Solid understanding of data warehousing and data modeling concepts
Experience managing and supporting production data environments
Familiarity with job scheduling, monitoring, and troubleshooting tools
Exposure to Azure Databricks or PySpark (preferred)
Nice to have
Experience with Node.js for building lightweight services or integrations
Exposure to cloud migration projects (on-prem to Azure)
Experience with CI/CD pipelines for data solutions