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GenAI with Python
2 months ago
We are seeking a highly skilled Big Data Engineer with strong expertise in Scala to join our dynamic team. The ideal candidate will have a deep understanding of big data technologies, proficiency in Scala, and experience in designing and implementing scalable data solutions. You will work closely with data scientists, analysts, and other engineers to build and maintain data pipelines, optimize data storage, and ensure data integrity.
Key Responsibilities- Data Pipeline Development: Design, build, and maintain scalable and reliable data pipelines using Scala and big data technologies such as Apache Spark, Apache Kafka, and Hadoop.
- Data Processing: Implement data transformation and aggregation processes, ensuring high performance and efficiency in processing large datasets.
- System Integration: Integrate various data sources and systems to streamline data flow and ensure seamless data availability across platforms.
- Optimization: Optimize data processing workflows, data storage, and query performance to handle large volumes of data efficiently.
- Collaboration: Work with cross-functional teams, including data scientists, analysts, and other engineers, to understand their data needs and deliver solutions that meet their requirements.
- Data Quality: Ensure data accuracy, consistency, and reliability by implementing data validation and monitoring processes.
- Documentation: Document data architectures, pipelines, and processes to maintain clear and comprehensive records.
- Troubleshooting: Identify and resolve issues related to data processing, system performance, and data quality.
- Education: Bachelor’s degree in computer science, Engineering, or a related field. Master’s degree is a plus.
- Experience: Minimum of 6 years of experience as a Big Data Engineer or in a similar role, with a strong focus on Scala and big data technologies.
- Skills:
- Programming Languages: Proficiency in Scala; experience with Java or Python is a plus.
- Big Data Technologies: Hands-on experience with Apache Spark, Apache Kafka, Hadoop, or similar technologies.
- Data Storage: Familiarity with distributed storage systems such as HDFS, AWS S3, or Azure Data Lake.
- Databases: Experience with relational and NoSQL databases (e.g., PostgreSQL, Cassandra, MongoDB).
- Tools: Knowledge of data integration and ETL tools (e.g., Apache Nifi, Talend).
- Version Control: Experience with Git or similar version control systems.
- Cloud Platforms: Experience with cloud services (e.g., AWS, Azure, Google Cloud) is a plus.
- Soft Skills:
- Strong problem-solving abilities and analytical skills.
- Excellent communication and teamwork skills.
- Ability to work independently and manage multiple tasks effectively.
- Experience with real-time data processing and stream processing frameworks.
- Knowledge of machine learning frameworks and data science concepts.
- Certification in big data technologies or cloud platforms.
