
Data Engineering Leader
2 days ago
The ideal candidate will leverage advanced data engineering techniques and analytics to drive business decisions. This role involves designing and building robust data pipelines, integrating structured and unstructured data from various sources, and developing tools for data processing and analysis.
Key Responsibilities:
- Develop comprehensive big data projects, including the development and deployment of innovative big data and AI applications.
- Ensure best practices are applied throughout all projects to maintain high-quality service and successful implementation.
- Assist project managers with estimating efforts and managing risks within customer proposals and statements of work.
- Design, develop, deploy, and document complex customer engagements, serving as the technical lead and authority.
- Facilitate knowledge transfer and provide training to team members, customers, and partners.
Requirements:
- 6+ years of experience in data engineering with expertise in Spark Scala, Python, and PySpark.
- In-depth knowledge of data architecture, including Spark Streaming, Spark Core, Spark SQL, and data modeling.
- Hands-on experience with various data management technologies and tools, such as Kafka, StreamSets, and MapReduce.
- Proficient in using advanced analytics and machine learning frameworks, including Apache Spark MLlib, TensorFlow, and PyTorch.
Databricks Skills:
- Extensive experience in data migration from on-premises to cloud environments.
- Skilled in designing and executing end-to-end data engineering solutions using Databricks.
- Proven hands-on experience with Databricks administration and operations.
Certifications:
- Databricks Engineering (Professional)
- Azure Data Engineer Associate
- Google Cloud Certified Professional
- AWS Solutions Architect Professional
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