Hadoop, Spark, Scala Data Engineer

Role & responsibilities

1

Design, develop, and maintain scalable data pipelines for extracting, transforming, and loading data from various sources to ensure seamless data flow and accessibility.

2

Collaborate with cross-functional teams to integrate data from multiple disparate sources, ensuring consistency, accuracy, and reliability of data.

3

Optimize data processing workflows and storage solutions for performance, scalability, and cost-efficiency

4

Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data throughout the data lifecycle.

5

Monitor the performance of data pipelines and infrastructure, identifying and resolving issues to maintain system stability and reliability.

Preferred candidate profile

• Strong experience in programming languages such as Python, Java, or Scala for data manipulation and engineering tasks.
• Expertise in SQL and NoSQL databases
• Hands-on experience with big data technologies like Hadoop, Spark, Kafka, and Hive to handle large-scale data processing and real-time data streams.
• In-depth knowledge of data warehousing solutions such as Amazon Redshift, Google BigQuery, and Snowflake for building and managing data warehouses.
• Proficiency in designing, developing, and maintaining ETL (Extract, Transform, Load) processes using tools like Apache NiFi, Talend, or Informatica.