Azure Data Engineer

Azure Data Engineer

Dallas TX (Hybrid 3 days in a week) 12+ Months Web Cam Interview

Requirement Notes (Candidate Job description below):

We need a senior (10+ years) Azure Data engineer with recent experience in Banking, Capital Markets or Financial services.

Candidates must have recent experience working with Azure Data Factory (ADF) and Azure Databricks in a Financial environment.

Candidates must have excellent communication skills/no accent.

Experience required on a resume and for submittal:

• How many years working with: Azure Data Engineer
• How many years working with: Azure Data Factory (ADF)
• How many years working with: Azure Databricks (highlighted expertise)
• How many years working with: Banking, Capital Markets or Financial services OR FORTUNE 500

Education:

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related field (Engineering or Math preferred).

Technical Skills:

Programming & Tools:

• 10+ years of experience in SQL, Python..Net is a plus.
• 3+ years of experience in Azure cloud services, including:
• Azure SQL Server
• Azure Data Factory (ADF)
• Azure Databricks (highlighted expertise)
• Azure Data Lake Storage (ADLS)
• Azure Key Vault
• Azure Functions
• Logic Apps
• 3+ years of experience in GIT and deploying code using CI/CD pipelines.

Certifications (Preferred):

• Microsoft Certified: Azure Data Engineer Associate
• Databricks Certified Data Engineer Associate or Professional

Soft Skills:

• Strong analytical and problem-solving skills.
• Excellent communication and interpersonal skills.
• Ability to work independently and collaboratively within a team.
• Attention to detail and a commitment to delivering high-quality work.

Responsibilities:

Data Pipeline Development:

• Create and manage scalable data pipelines to collect, process, and store large volumes of data from various sources.

Data Integration:

• Integrate data from multiple sources, ensuring consistency, quality, and reliability.

Database Management:

• Design, implement, and optimize database schemas and structures to support data storage and retrieval.

ETL Processes:

• Develop and maintain ETL (Extract, Transform, Load) processes to ensure accurate and efficient data movement between systems.

Data Warehousing:

• Build and maintain data warehouses to support business intelligence and analytics needs.

Performance Optimization:

• Optimize data processing and storage performance for efficient resource utilization and quick data retrieval.

Documentation:

• Create and maintain comprehensive documentation for data pipelines, ETL processes, and database schemas.

Monitoring and Troubleshooting:

• Monitor data pipelines and systems for performance and reliability, troubleshooting and resolving issues as they arise.

Technology Evaluation:

• Stay updated with emerging technologies and best practices in data engineering, evaluating and recommending new tools and technologies as appropriate.