Senior Technical Specialist

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.