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.