Azure Data Engineer

Job Summary :

As an Azure Data Engineer , you will design, implement, and optimize data architectures and data pipelines in the Microsoft Azure ecosystem. You will work with large datasets, develop data processing workflows, and integrate various data sources, ensuring the integrity, performance, and scalability of data systems. Your role will support data analytics, machine learning, and reporting capabilities within the organization.

Key Responsibilities :

1. Data Architecture and Design :

• Design and implement scalable and efficient data architectures in Azure using services like Azure Data Lake , Azure SQL Database , Azure Synapse Analytics , Azure Databricks , and Azure Blob Storage .
• Build and manage data pipelines for extracting, transforming, and loading (ETL) data from various sources to target systems.
• Implement and manage data models, ensuring that they meet the needs of business intelligence and analytics teams.

2. Data Integration and Pipeline Development :

• Develop robust data pipelines that move data from source systems (e.g., APIs, on-premises databases, cloud services) into Azure Data Storage and processing systems.
• Leverage Azure Data Factory to build, schedule, and automate ETL workflows for seamless data movement.
• Integrate structured, semi-structured, and unstructured data sources into the Azure environment.
• Implement data validation, data cleansing, and error-handling mechanisms in data pipelines.

3. Performance Optimization :

• Optimize the performance of data storage, processing, and querying to handle large volumes of data.
• Implement strategies for partitioning, indexing, and caching to ensure fast and efficient querying.
• Monitor data pipeline performance and troubleshoot any bottlenecks or issues.
• Ensure high availability and disaster recovery of data processing systems.

4. Data Security and Governance :

• Implement data security and privacy policies, ensuring data is encrypted both in transit and at rest.
• Enforce Azure RBAC (Role-Based Access Control) and other security practices to control data access.
• Ensure compliance with data governance standards, including data lineage, data quality, and auditing.
• Work with data stewards and governance teams to implement policies for metadata management and data cataloging.

5. Collaboration with Cross-Functional Teams :

• Collaborate with data scientists, business analysts, and other stakeholders to design and deliver data solutions that meet their needs.
• Work with cloud architects to design scalable, secure, and cost-effective cloud infrastructure for data storage and processing.
• Provide data-related support for business intelligence (BI), reporting, and machine learning models.
• Assist in designing data solutions that enable real-time analytics and reporting.

6. Data Quality and Monitoring :

• Monitor data pipelines for data quality and performance issues, proactively resolving any problems.
• Create automated data quality checks to ensure that data remains accurate, consistent, and reliable.
• Set up logging and monitoring tools (e.g., Azure Monitor , Log Analytics ) to keep track of pipeline health and performance.
• Conduct data audits and reconcile discrepancies to ensure data accuracy.

7. Documentation and Reporting :

• Document architecture, data flow diagrams, and data pipeline designs to maintain clear and understandable technical documentation.
• Generate reports and dashboards to track the performance of data pipelines and key metrics.
• Provide regular status updates to project managers and business stakeholders on pipeline progress, data quality, and system performance.

8. Continuous Improvement and Innovation :

• Continuously research and evaluate emerging technologies and best practices in data engineering, especially within the Azure ecosystem.
• Implement new features and optimizations to improve the overall data engineering process.
• Participate in team meetings and code reviews to ensure continuous learning and knowledge sharing.

Required Skills and Qualifications :

• Education : Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent work experience.
• Experience :
  • Proven experience as a Data Engineer with hands-on experience in building data pipelines, ETL processes, and data architectures.
  • Strong proficiency with Azure Data Services such as Azure Data Factory , Azure Synapse Analytics , Azure SQL Database , Azure Databricks , Azure Blob Storage , Azure Data Lake , and Azure Functions .
  • Strong knowledge of SQL , T-SQL , and other query languages for interacting with data stores and performing data manipulation tasks.
  • Experience with data integration, data warehousing, and cloud-based data architectures.
  • Proficiency with programming/scripting languages such as Python , Scala , Java , or PowerShell .
  • Familiarity with big data technologies like Hadoop , Spark , and Kafka (Azure Synapse integration with Spark or Azure HDInsight).

Preferred Skills :

• Azure Certifications (e.g., Microsoft Certified: Azure Data Engineer Associate ).
• Experience with data visualization tools like Power BI , Tableau , or Looker .
• Experience with DevOps practices and automation tools for deployment, testing, and monitoring.
• Familiarity with NoSQL databases (e.g., MongoDB , Cassandra ) or time-series databases (e.g., InfluxDB , Azure Time Series Insights ).
• Knowledge of machine learning workflows, including feature engineering and model deployment, in a cloud environment.
• Understanding of data privacy regulations such as GDPR and CCPA .

Personal Attributes :

• Strong analytical and problem-solving skills.
• Attention to detail and the ability to maintain high data quality standards.
• Ability to work in a fast-paced, team-oriented environment.
• Excellent communication skills to work effectively with technical and non-technical stakeholders.
• Proactive approach to identifying and resolving technical issues.