Azure Data Engineer

Job Title: Azure Data Engineer
Position: SE / SSE / LA
Experience: 5+ yrs.
Category: Software Development/ Engineering
Main location: Bangalore
Position ID: J1124-1080
Employment Type: Full Time / Permanent
Qualification: Bachelor's degree in Computer Science or related field

Job Description/ Position Description

The Azure Data Engineer is responsible for designing, implementing, and managing data pipelines and architectures on Microsoft Azure. The role involves working with Azure Databricks, SQL databases, and tools like Azure Data Factory (ADF) to transform, process, and integrate data for analytics and reporting. They collaborate with data scientists, business stakeholders, and other engineering teams to ensure reliable, scalable, and efficient data solutions.

Job Role/ future duties and responsibilities

• Data Pipeline Development: Design, build, and maintain scalable ETL (Extract, Transform, Load) pipelines using Azure Data Factory (ADF). Develop data transformation workflows using Databricks and PySpark to process large datasets.
• Data Integration: Implement data integration between multiple sources such as on-premise databases, cloud-based storage (Azure Blob Storage), and third-party APIs. Ensure smooth data flow across the Azure ecosystem.
• Data Modeling & Storage: Design and optimize SQL databases and Data Lake architectures to store and retrieve large datasets efficiently. Work with business analysts to translate data requirements into optimized storage and retrieval solutions.
• Data Analysis & Reporting: Create and optimize queries using SQL to support data analysis and reporting. Collaborate with data scientists to create datasets for machine learning models.
• Automation & Optimization: Write and maintain Python/PySpark scripts to automate data processing and cleaning tasks. Optimize and tune performance for data pipelines and databases to handle large-scale data efficiently.
• Collaboration: Work closely with data scientists, business analysts, and BI teams to ensure alignment on data needs and requirements.
• Communicate technical issues and data solutions effectively with both technical and non-technical stakeholders.
• Monitoring & Troubleshooting: Monitor data pipelines and ensure data quality, consistency, and security.
• Troubleshoot and resolve any issues or failures in data processing pipelines.

Required qualifications to be successful in this role

• Azure Data Platform: Proficiency in Azure Data Factory (ADF) for building ETL pipelines. Experience with Azure Databricks for data processing and analytics.
• SQL Expertise: Strong knowledge of SQL for querying and managing relational databases (e.g., Azure SQL Database, SQL Data Warehouse). Ability to optimize SQL queries and database performance.
• Programming Languages: Python and PySpark for writing data processing scripts and workflows. Understanding of object-oriented programming and coding best practices.
• Big Data Processing: Experience with PySpark and Databricks for large-scale data transformations and analysis.
• Data Integration: Knowledge of integrating data from diverse sources, such as APIs, databases, and cloud storage.
• Data Lakes & Storage: Familiarity with Azure Data Lake Storage and Blob Storage for storing raw and processed data.
• Data Governance & Security: Understanding of data security, governance, and best practices in cloud environments.
• Problem Solving & Debugging: Strong analytical and problem-solving skills for troubleshooting data pipeline and infrastructure issues.
• Version Control & CI/CD: Familiarity with version control (e.g., Git) and deploying code changes through CI/CD pipelines.

Technologies required:/ Selected Skills
Azure Data Factory (ADF)
Azure Databricks
Azure Data Lake Storage
Azure Blob Storage
Azure SQL Database
Azure Synapse Analytics (optional)
Azure Monitor & Log Analytics

Programming Languages:

• Python for developing custom data processing scripts and workflows.
• PySpark for distributed data processing using Spark in Databricks.
• SQL for querying, managing, and optimizing relational databases.