Data Specialist - R01569712

Experience Range: With at least 5-6 years of experience in data engineering, including substantial hands-on expertise in Databricks and PySpark

Key Responsibilities:
1. Design and implement robust ETL pipelines using Databricks and PySpark to efficiently process large-scale datasets
2. Develop, optimize, and maintain complex SQL queries, stored procedures, and scalable data models to support analytics and reporting
3. Collaborate with data architects and business stakeholders to translate requirements into high-performance data solutions
4. Integrate data from diverse sources into data warehouses and modern data platforms, ensuring data quality and consistency
5. Troubleshoot and resolve issues related to data ingestion, transformation, and storage within Databricks environments
6. Enforce best practices in data warehousing, data modeling, and Databricks platform fundamentals
7. Monitor and tune performance of data processing jobs for optimal throughput and reliability
8. Document data engineering processes and provide technical guidance to team members on Databricks and PySpark usage

Required Skills:
1. Advanced proficiency in Databricks platform
2. Expertise in PySpark for distributed data processing and transformation
3. Strong SQL skills for analytics and data manipulation
4. Experience with data warehousing architectures
5. Hands-on experience designing and maintaining stored procedures
6. Proficiency in Apache Spark for large-scale data processing

Preferred Skills:
1. Experience with Azure Databricks or AWS Glue
2. Familiarity with CI/CD pipelines for Databricks workflows
3. Knowledge of Databricks performance optimization techniques
4. Exposure to data governance and security best practices in cloud environments
5. Experience with real-time data streaming frameworks such as Apache Kafka

Desired Qualifications:
1. Bachelors degree in Computer Science, Information Technology, or a closely related discipline
2. Certification in Databricks or Apache Spark
3. Certification in SQL or data warehousing technologies