Job Description: Seeking a skilled Data Engineer with a robust background in PySpark and extensive experience with AWS services, including Athena and EMR.
The ideal candidate will be responsible for designing, developing, and optimizing large-scale data processing systems, ensuring efficient and reliable data flow and transformation.
Key Responsibilities: · Data Pipeline Development: Design, develop, and maintain scalable data pipelines using PySpark to process and transform large datasets.
· AWS Integration: Utilize AWS services, including Athena and EMR, to manage and optimize data workflows and storage solutions.
· Data Management: Implement data quality, data governance, and data security best practices to ensure the integrity and confidentiality of data.
· Performance Optimization: Optimize and troubleshoot data processing workflows for performance, reliability, and scalability.
· Collaboration: Work closely with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet business needs.
· Documentation: Create and maintain comprehensive documentation of data pipelines, ETL processes, and data architecture.
Required Skills and Qualifications: · Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
· Experience: 5 years of experience as a Data Engineer or in a similar role, with a strong emphasis on PySpark.
· Technical Expertise: o Proficient in PySpark for data processing and transformation.
o Extensive experience with AWS services, specifically Athena and EMR.
o Strong knowledge of SQL and database technologies.
o Experience with Apache Airflow is a plus o Familiarity with other AWS services such as S3, Lambda, and Redshift.
· Programming: Proficiency in Python; experience with other programming languages is a plus.
· Problem-Solving: Excellent analytical and problem-solving skills with attention to detail.
· Communication: Strong verbal and written communication skills to effectively collaborate with team members and stakeholders.
· Agility: Ability to work in a fast-paced, dynamic environment and adapt to changing priorities.
Preferred Qualifications: · Experience with data warehousing solutions and BI tools.
· Knowledge of other big data technologies such as Hadoop, Hive, and Kafka.
· Understanding of data modeling, ETL processes, and data warehousing concepts.
· Experience with DevOps practices and tools for CI/CD.