What are the responsibilities and job description for the Azure Data Engineer position at K&K Global Talent Solutions INC.?
K&K Global Talent Solutions Inc is an International recruiting agency that has been providing technical resources in the USA region since 1993.
This position is with one of our clients in The USA, who is actively hiring candidates to expand their teams.
Job Title: Azure Data Engineer
Location: Dallas, TX
Job Description:
We are seeking a skilled Azure Data Engineer to design, develop, and optimize data solutions on the Azure platform. The ideal candidate will have experience in data integration, ETL processes, and cloud-based data warehousing to support business intelligence and analytics initiatives.
Key Responsibilities:
- Design, implement, and maintain Azure-based data solutions, including Azure Data Factory, Azure Synapse Analytics, and Azure Data Lake Storage.
- Develop ETL/ELT pipelines for data ingestion, transformation, and integration from multiple sources.
- Optimize data storage, indexing, and retrieval for high-performance analytics.
- Implement data security, governance, and compliance best practices.
- Work with SQL, Python, Spark, and Databricks to process large datasets efficiently.
- Monitor and troubleshoot data pipelines and performance issues.
- Collaborate with business teams to understand data needs and build scalable solutions.
Required Qualifications:
- 3 years of experience as a Data Engineer with expertise in Azure cloud services.
- Hands-on experience with Azure Data Factory, Azure SQL Database, Azure Data Lake, and Synapse Analytics.
- Strong knowledge of SQL, Python, and Spark for data transformation.
- Experience with Databricks and big data processing frameworks.
- Familiarity with Power BI or other visualization tools is a plus.
- Understanding of CI/CD pipelines and DevOps for data engineering.
Preferred Qualifications:
- Azure certifications (DP-203: Azure Data Engineer Associate).
- Experience in real-time streaming solutions (Kafka, Event Hubs).
- Exposure to machine learning and AI-driven analytics.