Azure Data Engineer - S

Key Responsibilities

Data Engineering & Pipeline Development

• Design, develop, and optimize scalable ETL/ELT pipelines using Azure data services.
• Build and maintain data ingestion frameworks for structured, semi-structured, and unstructured data.
• Develop end-to-end data workflows to support reporting, analytics, and AI/ML use cases.
• Implement data transformation and data quality processes across multiple data sources.
• Automate data integration and orchestration workflows.

Azure Cloud Data Platform

• Design and implement modern data architectures including Data Lake, Data Warehouse, and Lakehouse solutions.
• Utilize Azure-native services to build secure and scalable data ecosystems.
• Develop solutions using:
• Azure Data Factory (ADF)
• Azure Synapse Analytics
• Azure Data Lake Storage Gen2 (ADLS)
• Azure Databricks
• Azure SQL Database
• Azure Event Hub
• Azure Stream Analytics
• Support migration of on-premises data environments to Azure Cloud.

Data Modeling & Storage

• Design and maintain logical and physical data models.
• Implement dimensional modeling techniques including Star and Snowflake schemas.
• Optimize data storage, partitioning, and performance strategies.
• Ensure efficient querying and reporting capabilities.

Big Data & Analytics

• Process and analyze large-scale datasets using Databricks and Apache Spark.
• Build batch and real-time data processing pipelines.
• Enable advanced analytics, AI, and machine learning workloads by delivering trusted data assets.
• Collaborate with Data Scientists, BI Developers, and Business Analysts.

Security & Governance

• Implement data security, privacy, and governance standards.
• Configure RBAC, managed identities, and data access controls.
• Ensure compliance with organizational and regulatory data policies.
• Monitor data quality, lineage, and auditing requirements.

Performance Optimization & Support

• Troubleshoot pipeline failures and optimize data processing performance.
• Monitor Azure data workloads and implement cost optimization strategies.
• Ensure high availability, reliability, and scalability of data solutions.
• Support production incidents and perform root-cause analysis.

Required Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or related field.
• 4+ years of experience in Data Engineering with 2+ years of Azure cloud experience.
• Strong proficiency in SQL and Python.
• Hands-on experience with:
• Azure Data Factory (ADF)
• Azure Databricks
• Azure Synapse Analytics
• Azure Data Lake Storage (ADLS Gen2)
• Azure SQL Database
• Experience building ETL/ELT pipelines and data integration solutions.
• Strong understanding of data warehousing and dimensional modeling concepts.
• Experience with Apache Spark and distributed data processing.
• Knowledge of CI/CD processes and DevOps practices