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.
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Syntricate Technologies Inc