Description

You will lead the design, implementation, and delivery of end-to-end data engineering solutions on AWS.

Responsibilities

• Build automated ETL/ELT pipelines to ingest data from REST APIs, relational databases, and file systems into AWS data lakes and Lakehouse architectures.
• Own the full development lifecycle including coding, debugging, performance tuning, and deploying applications to production environments.
• Design data solutions covering ingestion, storage, integration, processing, and access using AWS services.
• Conduct end-to-end verification and validation for all data applications and manage data migration tasks.
• Collaborate within an Agile methodology to deliver scalable data infrastructure.

Required Skills

• 8+ years of experience in Data Engineering and Big Data concepts.
• Expertise in SQL, Python, and PySpark.
• Extensive experience with AWS services including Glue, EMR, S3, Aurora, RDS, and Redshift.
• Hands-on experience with Snowflake and AWS architecture.
• Proficiency in building data pipelines from REST APIs to S3 and relational databases.
• Strong ability to write and analyze SQL stored procedures.
• Bachelor's degree in Computer Science.
• Experience using Git or Bitbucket for version control and remote collaboration.

Preferred Skills

• Experience with DevOps tools and practices, including Jenkins and CI/CD pipelines.
• Proven track record with large-scale cloud data migrations.

Key Skills

Education

Bachelor's degree in Computer Science