Need _ Sr. Data Engineer _ Dallas, TX(Hybrid)

Role: Sr. Data Engineer

Location: Dallas (Hybrid)

Business Domain: Financial Services, Private Equity, Asset Management, Commercial Real Estate and/or Commercial Credit experience preferred

Senior Data Engineer

Education:

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

Technical Skills:

Programming & Tools:

5+ 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:

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

1. Data Integration:

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

1. Database Management:

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

1. ETL Processes:

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

1. Data Warehousing:

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

1. Performance Optimization:

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

1. Documentation:

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

1. Monitoring and Troubleshooting:

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

1. Technology Evaluation:

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