Summary

We are looking for a few skilled and motivated Data Engineers to work on our client projects. The ideal candidates should have a strong background in data engineering, be passionate about working with data, and have excellent problem-solving skills.

Responsibilities

• Develop, construct, test, and maintain data architectures, including databases, large scale data processing systems, and data pipelines
• Collaborate and communicate with client contact persons to understand the data processing requirements, design and develop the required solutions and deliver them
• Design and implement efficient and scalable ETL processes to ingest and process data from various sources, including APIs, files, databases and the web
• Optimize data architectures for performance, reliability, and cost-efficiency using AWS tools and services
• Ensure data quality, consistency, and security by implementing proper data validation, cleansing, and monitoring processes
• Develop and maintain data models and schema designs to support the storage and retrieval of structured and unstructured data
• Stay current with emerging data engineering tools, techniques, and best practices, and contribute to the continuous improvement of our data engineering processes and standards

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field
• 1-2 years of hands-on experience in data engineering or a related role
• Strong programming skills in Python and familiarity with PySpark
• Hands-on experience with ETL processes, data pipeline development, and workflow management using tools such as AWS Glue, Airflow, etc
• Proficiency in SQL and experience working with relational databases, such as PostgreSQL, MySQL, or SQL Server
• Familiarity with AWS tools and services for data storage and processing, such as S3 and EC2
• Excellent problem-solving skills and attention to detail
• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams

Nice-to-Haves:

• Experience with NoSQL databases, such as MongoDB, Cassandra, or Couchbase
• Knowledge of data warehousing concepts and technologies, such as Snowflake or Redshift
• Familiarity with data visualization tools, such as Tableau, Power BI, or Looker