Title: 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.
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