HYDERABAD · FULLTIME
Lead/Senior Azure Data Engineer - Hyderabad
RDSolutions India
Hyderabad · onsite · Posted 1d ago
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Section · 01
About this role
Job Title: Lead/Senior Azure Data Engineer
Employment Type: Full Time, Permanent Position Location: Hyderabad
Qualifications: BE/B.Tech/MCA Degree in Computer Science, Engineering, or similar relevant field Total Experience: 7-10 years
Working Model: Work from office About the Role We are seeking Data Engineers to design, develop, and support scalable, cloud-native data platforms on Microsoft Azure. The ideal candidate will have strong experience in building modern data pipelines, event-driven architectures, and Lakehouse solutions using Azure data services. You will work closely with cross-functional teams to develop secure, reliable, and high-performance data solutions that enable analytics, reporting, and real-time data processing.
Key Responsibilities
- Design, develop, and maintain scalable data ingestion and transformation pipelines using Azure Data Factory, Azure Functions, Event Hubs, and Azure Data Lake Storage Gen2.
- Build and support modern Lakehouse architectures following the Medallion (Raw, Valid, Curated) data model.
- Develop and maintain real-time and event-driven data processing solutions using streaming technologies and Change Data Capture (CDC) patterns.
- Create and optimize SQL-based data transformation, validation, and quality processes.
- Develop production-grade Python applications for data processing, automation, and event-driven workloads.
- Design, develop, and deploy Power BI dashboards, semantic models, and reporting solutions connected to curated Lakehouse datasets.
- Manage Infrastructure as Code (IaC) using Terraform to provision and maintain Azure resources.
- Build and maintain Azure DevOps CI/CD pipelines for automated deployment, testing, and release management.
- Implement source control best practices, including feature branching, pull request reviews, and structured code management.
- Collaborate with architects, developers, and business stakeholders to deliver scalable and secure data solutions.
- Monitor, troubleshoot, and optimize platform performance, reliability, and operational efficiency.
- Ensure adherence to data governance, security, and cloud engineering best practices.
Required Qualifications
- Strong hands-on experience with Azure Data Services including: Azure Data Factory (ADF) Azure Event Hubs Azure Functions Azure Data Lake Storage Gen2 (ADLS Gen2) Azure Key Vault Azure Service Bus
- Strong experience in designing and implementing Lakehouse and Medallion Architecture.
- Expertise in Power BI development including dashboards, semantic models, deployment pipelines, and integration with Lakehouse data sources.
- Experience with Infrastructure as Code (Terraform) for Azure resource provisioning.
- Strong experience building Azure DevOps CI/CD pipelines with multi-stage deployments and release management.
- Experience designing and implementing streaming/event-driven data pipelines.
- Strong SQL (T-SQL) development for transformation, validation, and optimization.
- Proficiency in Python (3.10+) for production-grade data engineering applications.
- Strong understanding of Git, feature branching strategies, pull requests, and version control best practices.
- Excellent analytical, troubleshooting, and problem-solving skills.
- Strong communication and collaboration skills in Agile development environments.
Preferred Qualifications
- Experience deploying and managing applications on Azure Kubernetes Service (AKS) including Helm charts.
- Knowledge of secure data engineering practices including PII handling, tokenization, Managed Identity, RBAC, private networking, and Azure security best practices.
- Experience implementing observability using OpenTelemetry, Azure Monitor, and Application Insights.
- Experience working with Debezium or other Change Data Capture (CDC) technologies.
- Knowledge of automated testing frameworks including pytest, integration testing, and contract testing.
- Experience with Python dependency management, packaging, and reproducible deployment practices.
- Exposure to multi-repository enterprise data platforms and collaborative development models.
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Section · 02