Job Description

Required Candidate Location: Dallas, TX Hybrid/3 days a week

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.

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:

1. Data Pipeline Development:
2. Create and manage scalable data pipelines to collect, process, and store large volumes of data from various sources.
3. Data Integration:
4. Integrate data from multiple sources, ensuring consistency, quality, and reliability.
5. Database Management:
6. Design, implement, and optimize database schemas and structures to support data storage and retrieval.
7. ETL Processes:
8. Develop and maintain ETL (Extract, Transform, Load) processes to ensure accurate and efficient data movement between systems.
9. Data Warehousing:
10. Build and maintain data warehouses to support business intelligence and analytics needs.
11. Performance Optimization:
12. Optimize data processing and storage performance for efficient resource utilization and quick data retrieval.
13. Documentation:
14. Create and maintain comprehensive documentation for data pipelines, ETL processes, and database schemas.
15. Monitoring and Troubleshooting:
16. Monitor data pipelines and systems for performance and reliability, troubleshooting and resolving issues as they arise.
17. Technology Evaluation:
18. Stay updated with emerging technologies and best practices in data engineering, evaluating and recommending new tools and technologies as appropriate.

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