
Data Engineer
5 days ago
This role is for one of the Weekday's clients
We are seeking a highly skilled Data Engineer with a strong background in building and managing scalable data pipelines. You will play a critical role in designing, developing, and optimizing data infrastructure to support analytics and real-time processing needs. This role requires expertise in relational and NoSQL databases, data warehouses, real-time streaming, ETL processes, and cloud-based data solutions.
Requirements
Key Responsibilities- Design, develop, and optimize scalable data pipelines for structured and unstructured data.
- Work with relational databases (MySQL, PostgreSQL) and NoSQL databases (MongoDB, Cassandra) to manage and process large datasets efficiently.
- Build and maintain data pipelines with data warehouses like Snowflake, Redshift for efficient storage and retrieval.
- Process unstructured data stored in S3 using Athena, Glue, and other AWS services to derive meaningful insights.
- Implement real-time data streaming and messaging solutions using Kafka to enable low-latency data processing.
- Develop ETL pipelines to ensure smooth data integration, transformation, and optimization for analytics and reporting.
- Write high-performance code using Python, Java, or Scala to process and analyze large datasets efficiently.
- Utilize Apache Spark for big data processing and analytics to enhance data engineering workflows.
- Work with cloud platforms like AWS, GCP, or Azure to build scalable and resilient data infrastructure.
- 5+ years of experience in data engineering, with a focus on data pipeline development, data processing, and optimization.
- Strong expertise in Relational Databases (MySQL, PostgreSQL) and NoSQL databases (MongoDB, Cassandra).
- Experience working with data warehouses such as Snowflake and Redshift for analytical workloads.
- Hands-on experience in processing unstructured data using tools like Athena, Glue, and S3.
- Proficiency in real-time data streaming using Kafka for handling large-scale event-driven data processing.
- Strong knowledge of ETL processes, data integration techniques, and pipeline performance optimization.
- Proficiency in programming languages like Python, Java, or Scala for data processing and automation.
- Experience with Apache Spark for distributed big data processing is a plus.
- Familiarity with cloud platforms (AWS, GCP, or Azure) for data infrastructure management is an advantage.
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