Azure Data Engineer(Banking or Financial)

Title: Azure Data Engineer(Banking or Financial) Location: Dallas, TX Hybrid
Duration: Long Term Contract

Must have :: 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.

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.

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.