Senior Technical Specialist

Title: Senior Azure Data Engineer

Location: India ( Remote )

Education: Bachelor’s Degree

Experience Level: 10+

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.