
Senior Cloud Data Specialist
1 week ago
An experienced Data Engineer will be responsible for designing, developing, and maintaining large-scale data processing solutions using PySpark and Python. They will collaborate with cross-functional teams to deliver data-driven solutions.
">- Design, develop, and optimize data pipelines using PySpark, Spark, and Python.
- Implement ETL/ELT solutions to integrate structured and unstructured data from multiple sources.
- Develop and maintain Hive queries and ensure seamless data transformations.
- Write and optimize SQL queries for large datasets.
- Build, monitor, and support real-time streaming solutions using Apache Kafka.
- Work on Databricks to create, manage, and optimize data workflows.
- Deploy and manage solutions in Azure cloud services (e.g., Data Lake, Synapse, Azure Databricks).
- Debug, troubleshoot, and optimize code for performance and scalability.
The ideal candidate will have 3–4 years of experience in data engineering with a focus on PySpark and Python. They will possess strong knowledge of Spark, Hive, SQL, and Unix shell scripting.
- 3–4 years of experience in data engineering with a focus on PySpark and Python.
- Strong knowledge of Spark, Hive, SQL, and Unix shell scripting.
- Experience with Databricks in a production environment.
- Hands-on experience with Azure cloud services (preferred: Data Lake, Synapse, Databricks).
- Experience working with Apache Kafka or other real-time streaming technologies.
- Strong problem-solving and debugging skills.
- Excellent written and verbal communication skills.
- Ability to work independently in a fast-paced environment and adapt to new technologies quickly.
The following skills are highly desirable:
- Exposure to CI/CD pipelines for data solutions.
- Knowledge of data governance and security best practices.
- Experience with Agile/Scrum methodology.
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