Title: Senior Azure Data Engineer Location: India ( Remote ) Education: Bachelor’s Degree Experience Level: 10+ We are looking for experienced Data Engineers who can provide overlap with North America business hours. The ideal coverage is until approximately 3:00 PM–4:00 PM Eastern Time (ET). Resources may also work staggered shifts to ensure broader support coverage and operational continuity. We are seeking a highly skilled, hands-on Senior Data Engineer with strong expertise in SQL Server, Azure Data Factory (ADF), ETL development, and Data Warehousing. This role is focused on designing, developing, troubleshooting, and modernizing enterprise-scale data integration solutions. The ideal candidate is a true Data Engineer, not primarily a BI or reporting developer. They should be capable of independently analyzing existing data pipelines, resolving production issues, building scalable data solutions, and driving the modernization of legacy SSIS-based workloads to Azure-based data platforms. Required Qualifications: Technical Skills • 8+ years of experience in Data Engineering, ETL Development, or Data Warehousing. • Strong expertise in Microsoft SQL Server and advanced T-SQL development. • Strong hands-on experience with Azure Data Factory (ADF). • Extensive experience designing and building ETL/ELT pipelines. • Strong experience with SSIS development, troubleshooting, migration, and modernization. • Solid understanding of Data Warehouse concepts and dimensional modeling, including: • Fact & Dimension models • Star Schema • Snowflake Schema • Experience processing large-volume data and performing SQL performance tuning. • Experience integrating with REST APIs and external data providers. • Experience with SFTP and file-based integrations. • Strong knowledge of data validation, reconciliation, and quality controls. • Proven production support and troubleshooting skills. • Experience using Git, source control practices, CI/CD, and deployment automation. • Experience working across on-premises, hybrid, and cloud data platforms. Preferred AI / GenAI Experience: • Candidates with exposure to AI-enabled data engineering solutions will be highly valued. • Experience incorporating AI/GenAI capabilities into data engineering solutions. • Experience with Azure OpenAI Service or OpenAI API integrations. • Python-based AI automation and intelligent workflow development. • AI-assisted data quality monitoring, anomaly detection, and operational troubleshooting. • Understanding of: • Retrieval-Augmented Generation (RAG) • Embeddings • Vector Databases Additional Preferred Skills: • Python • PySpark • Databricks • Snowflake • Azure Synapse Analytics • Microsoft Fabric • MongoDB / NoSQL databases • PowerShell scripting • Data Lake and Lakehouse architectures Desired Candidate Profile The successful candidate will: • Be highly hands-on and technically strong in data engineering. • Demonstrate the ability to work independently with minimal supervision. • Quickly understand and troubleshoot complex legacy and modern data platforms. • Possess strong analytical and problem-solving skills. • Have experience supporting mission-critical production environments. • Be comfortable driving modernization initiatives and recommending best practices. • Communicate effectively with both technical and business stakeholders. • Take ownership of deliverables and operational outcomes. Nice-to-Have Industry Experience: • Enterprise Data Warehousing • Financial Services • Healthcare • Retail & Consumer Data Platforms • SaaS and Cloud-Native Data Environments Key Responsibilities: • Design, develop, and maintain scalable ETL/ELT solutions and data pipelines. • Support and enhance enterprise data warehouse solutions. • Analyze existing SSIS packages and lead modernization efforts toward Azure Data Factory and modern cloud-based architectures. • Build and optimize data ingestion, transformation, and integration processes. • Perform advanced SQL development, query optimization, and performance tuning for large-volume datasets. • Develop integrations with REST APIs, third-party systems, and file-based interfaces. • Support SFTP-based and batch data exchange processes. • Troubleshoot production issues, perform root-cause analysis, and implement preventive solutions. • Ensure data quality through reconciliation, validation, monitoring, and exception handling. • Collaborate with business and technical stakeholders to gather requirements and build data solutions. • Implement CI/CD pipelines and source control best practices for data engineering solutions. • Support hybrid environments involving on-premises and cloud-based systems. • Participate in architecture discussions and recommend modernization strategies.