We are seeking a highly skilled Senior Data Engineer with deep expertise in Microsoft Azure data ecosystem to design, develop, and maintain scalable data pipelines and architectures. The ideal candidate will play a key role in building robust data solutions that support advanced analytics, BI, and AI workloads across the organization. This role involves working with cross-functional teams — data scientists, analysts, and business stakeholders — to ensure high-quality, secure, and performant data delivery.
Total experience : 8 to 10 years
Key Responsibilities:
Job Overview:We are seeking a highly skilled Senior Data Engineer with deep expertise in Microsoft Azure data ecosystem to design, develop, and maintain scalable data pipelines and architectures. The ideal candidate will play a key role in building robust data solutions that support advanced analytics, BI, and AI workloads across the organization. This role involves working with cross-functional teams — data scientists, analysts, and business stakeholders — to ensure high-quality, secure, and performant data delivery.Total experience : 8 to 10 years Key Responsibilities:
1. Data Architecture & Pipeline Development
Design, develop, and maintain data ingestion, transformation, and storage pipelines using Azure Data Factory, Azure Databricks, and Synapse Analytics.
Build end-to-end ETL/ELT workflows from diverse sources such as APIs, on-prem databases, SaaS applications, and data lakes.
Implement data modeling (star schema, snowflake, data vault, etc.) for analytical and operational data stores.
Manage data ingestion frameworks for both batch and streaming (real-time) use cases using Azure Event Hubs / Azure Stream Analytics / Kafka.
2. Data Management & Governance
Ensure data quality, consistency, and integrity across all environments.
Implement and enforce data governance standards, including metadata management, lineage tracking, and data cataloging (e.g., Azure Purview).
Manage security, compliance, and access controls using Azure Active Directory (AAD) and RBAC principles.
Automate data validation, auditing, and monitoring workflows using modern DevOps practices.
3. Cloud & DevOps Integration
Manage and optimize Azure Data Lake Storage (ADLS Gen2) for cost and performance.
Leverage Infrastructure as Code (IaC) tools such as Terraform, Bicep, or ARM templates for environment provisioning.
Implement CI/CD pipelines for data workflows using Azure DevOps / GitHub Actions.
Optimize compute and storage costs while maintaining scalability and resilience.
4. Collaboration & Leadership
Partner with data scientists, BI developers, and product teams to deliver reliable data solutions.
Mentor junior data engineers on best practices, coding standards, and Azure services.
Participate in architectural reviews and contribute to data platform design decisions.
Communicate technical insights and trade-offs to non-technical stakeholders effectively.
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