Data Engineer

Job Overview:

The AWS Data Engineer will be responsible for designing, building, and maintaining scalable data pipelines and architectures on AWS. This role involves working with cross-functional teams to ensure data integrity, optimize data processing, and support business intelligence initiatives. The ideal candidate is proficient in AWS services, data warehousing, and ETL processes, and has a strong understanding of big data technologies.

Key Responsibilities:

• Design, develop, and manage data pipelines to support analytics, reporting, and machine learning initiatives.
• Implement scalable and efficient ETL processes to move and transform large volumes of data.
• Work with AWS cloud services, including but not limited to Amazon S3, Redshift, Glue, EMR, Lambda, and Kinesis.
• Build and optimize data lakes and data warehouses, ensuring high data availability and quality.
• Develop and manage data infrastructure for real-time and batch data processing.
• Ensure data integrity and security throughout the data lifecycle.
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions.
• Monitor, troubleshoot, and optimize data processing workflows for performance and cost efficiency.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, or a related field.
• AWS Certified Data Analytics – Specialty or AWS Certified Big Data – Specialty (preferred).
• 2-3 years of experience as a Data Engineer or Data Engineer Intern.
• Strong experience with AWS data services (e.g., S3, Redshift, Glue, EMR, Lambda, Kinesis).
• Proficiency in SQL and experience with data warehousing solutions (e.g., Amazon Redshift, Snowflake).
• Hands-on experience with big data processing tools and frameworks such as Apache Spark, Hadoop, or Kafka.
• Expertise in ETL and data pipeline development, data integration, and data transformation.
• Knowledge of data modeling, data architecture, and data governance principles.
• Familiarity with Python, Scala, or Java for data processing.
• Strong understanding of data security and compliance in cloud environments.

Preferred Skills:

• Experience with data lake architecture and implementing data lakes on AWS.
• Knowledge of containerization (Docker) and orchestration (Kubernetes) for scalable data solutions.
• Experience with CI/CD pipelines and version control (e.g., Git, Jenkins).
• Strong analytical and problem-solving skills.
• Ability to communicate complex technical concepts to non-technical stakeholders.