Data Engineer

Job Description

Overview

We are seeking a highly experienced Data Engineer with 5+ years of experience. This role is critical to hitting product rollout deadlines, as the team's work is a hard, direct dependency for other product feature rollouts. The ideal candidate will be a hands-on developer with deep expertise in the AWS data stack, focusing primarily on data engineering and pipeline development.

Key Responsibilities

• Develop and Implement Data Pipelines: Design, build, and maintain robust data pipelines primarily using AWS Glue and PySpark.
• Data Sourcing and Transformation: Source data from various systems, including Redshift and Aurora, performing necessary streaming transformations and heavy data cleaning.
• Data Delivery: Push resulting, cleaned datasets into S3 buckets.
• External Integration: Manage the secure transfer of resulting files via SFTP to an external 3rd party company's server, adhering to non-negotiable external integration deadlines.
• Collaboration: Work closely with the team to consult on the best and most efficient solutions for achieving required data outputs, given the constraints of the AWS Glue/PySpark environment.

Required Qualifications and Skills

• AWS Data Stack: Heavy expertise in the AWS ecosystem, specifically AWS Glue.
• PySpark Expertise: Hands-on experience working with PySpark on complex application implementations is required.
• Database Knowledge: Heavy knowledge of both relational (e.g., Redshift, Aurora) and non-SQL databases, and how to leverage them within the AWS Glue/PySpark environment.
• Experience Level: Looking for experienced engineers with .
• Data Engineering Fundamentals: Strong general knowledge of how to efficiently get, transform, and push out data.

Job Responsibilities

Key Responsibilities

• Develop and Implement Data Pipelines: Design, build, and maintain robust data pipelines primarily using AWS Glue and PySpark.
• Data Sourcing and Transformation: Source data from various systems, including Redshift and Aurora, performing necessary streaming transformations and heavy data cleaning.
• Data Delivery: Push resulting, cleaned datasets into S3 buckets.
• External Integration: Manage the secure transfer of resulting files via SFTP to an external 3rd party company's server, adhering to non-negotiable external integration deadlines.
• Collaboration: Work closely with the team to consult on the best and most efficient solutions for achieving required data outputs, given the constraints of the AWS Glue/PySpark environment.