Azure Data Engineer CTR

Title:Azure Data Engineer

Location:India ( Remote )

Education:Bachelors 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 PM4:00 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.