Job Title: Senior Azure Data Engineer (12+ Years Only!!)
Location: Dallas, TX (Must be Local)
Work Mode: Hybrid – 3 days/week onsite
Visa: USC/GC –
NOTE---
THE MANAGER WANTS TO SEE MORE SENIOR CANDIDATES THEN 10 YEARS*** We need: A senior (12+ years) Azure Data Analyst with extensive experience working with the Azure Data suite listed below. THE CLIENT WOULD LIKE TO SEE CERTIFICATIONS.
****CANDIDATES MUST HAVE CURRENT CAPITAL MARKETS/TRADING AND/OR HEDGE FUND EXPERIENCE AND EXCELLENT COMMUNICATION SKILLS. HOT & MOVING FAST!!****
- Azure SQL Server
- Azure Data Factory (ADF)
- Azure Databricks (highlighted expertise)
- Azure Data Lake Storage (ADLS)
- Azure Key Vault
- Azure Functions
- Logic Apps
**Candidates must have Long Projects/Good Tenure, Excellent communication skills and a State issued ID (Not Bills) showing they are Local.
Job Description:
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:
- 10+ years of experience in SQL, Python. .Net is a plus.
- 5+ 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
- 5+ 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.
