Job Description

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.

Job Tags

3 days per week

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