Qualifications

• 5-7 years of data engineering experience with strong knowledge of ETL and data warehousing principles.
• Proficient in Java, particularly for big data processing, with a strong focus on Spark and Hadoop environments.
• Advanced SQL skills with the ability to write complex queries and optimize for performance.
• In-depth experience working with AWS services like S3, EMR, Redshift, and DynamoDB.
• Familiar with Bash scripting for automation and data manipulation tasks.
• Exposure to Kafka, Spark, and the Hadoop ecosystem is essential; experience with Databricks is a plus.
• A Bachelor's or Master's degree in Computer Science or a related field is preferred.

Responsibilities

• Design and build robust data pipelines to support data warehousing and analytical solutions.
• Develop and optimize ETL processes, ensuring data quality and integrity throughout the lifecycle.
• Collaborate with data scientists and analysts to enable access to structured and unstructured data.
• Manage and troubleshoot AWS services and frameworks related to big data processing.
• Write and optimize complex SQL queries for data extraction and reporting needs.
• Implement and maintain best practices for data governance and security in line with clearance requirements.
• Ensure the scalability and performance of data solutions, including using tools like Databricks.

Benefits

• Opportunity to work in a cutting-edge tech environment supportive of professional development.
• Contribute to significant projects that impact data accessibility and intelligence for decision-making.
• Flexible working conditions that foster work-life balance.
• Access to industry-leading tools and technologies for data engineering.
• Potential for career advancement within a dynamic and innovative team.