Azure Senior Data Engineer - S

Key Responsibilities

• Design, develop, and optimize end-to-end data pipelines and data integration solutions on Azure.
• Build scalable data ingestion, transformation, and processing frameworks using Azure-native services.
• Develop and maintain ETL/ELT processes for structured and unstructured data.
• Design and implement modern data lake, data warehouse, and lakehouse architectures.
• Collaborate with business analysts, data scientists, architects, and application teams to understand data requirements.
• Ensure data quality, governance, security, and compliance standards are maintained.
• Optimize data processing performance, scalability, and cost efficiency.
• Implement data modeling strategies including dimensional and relational models.
• Develop solutions for batch, near real-time, and streaming data processing.
• Automate deployment and monitoring processes using DevOps and Infrastructure as Code principles.
• Troubleshoot and resolve data pipeline and platform issues.
• Create and maintain technical documentation, standards, and best practices.
• Mentor junior team members and contribute to data engineering excellence initiatives.

Required Skills & Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, Mathematics, or related field.
• 8+ years of experience in Data Engineering, Data Warehousing, or Big Data solutions.
• 5+ years of hands-on experience with Microsoft Azure Data Services.
• Strong experience in designing and developing enterprise-scale data pipelines.
• Expertise in SQL and database performance tuning.
• Strong knowledge of data warehousing concepts and dimensional modeling.
• Hands-on experience with Azure data services including:
• Azure Data Factory (ADF)
• Azure Synapse Analytics
• Azure Data Lake Storage (ADLS)
• Azure Databricks
• Azure SQL Database
• Azure Event Hub
• Azure Stream Analytics
• Azure Functions
• Azure Key Vault
• Microsoft Fabric (preferred)
• Experience with big data processing frameworks and distributed computing.
• Strong programming skills in Python, PySpark, Scala, or SQL.
• Experience working with structured, semi-structured, and unstructured data.
• Familiarity with CI/CD pipelines and DevOps practices.
• Strong troubleshooting and analytical skills.