Senior AWS Data Engineer | ETL, PySpark & Redshift

Artifintech; Chicago (IL); On-site; USD 120,000 - 160,000; Full time

Job summary

Artifintech is seeking a Senior ETL Developer to design, build, and optimize enterprise-scale data pipelines within the AWS ecosystem, leveraging Python and PySpark on large datasets.

You will implement robust ETL processes with AWS Glue, S3, Redshift, Lambda, and Step Functions; write advanced SQL; and ensure data quality, profiling, and scheduling while embracing CI/CD using Harness, YAML, and CloudFormation.

Qualifications

• 6+ years of ETL development and data integration experience.
• Strong SQL proficiency with complex query optimization.
• 3–4 years using Python/PySpark on large-scale datasets.
• 3–4 years of hands-on AWS experience (Glue, S3, Redshift, Lambda, Step Functions, RDS, CloudWatch, SNS, SQS, EventBridge).
• 3+ years in Big Data processing and scalable data architectures.
• DevOps/CI/CD experience with Harness, YAML, and CloudFormation.

Responsibilities

• Design, develop, implement, and optimize enterprise ETL pipelines and data processing solutions.
• Build scalable data pipelines using AWS Glue, Python, PySpark, and SQL.
• Develop complex SQL queries, stored procedures, and data transformation workflows.
• Collaborate with architects, developers, and stakeholders to deliver enterprise data solutions.
• Provide production support and root-cause analysis for ETL processes.
• Maintain coding standards and ETL development best practices.

Skills

ETL

SQL

Python

PySpark

AWS Glue

AWS services

Data modeling

CI/CD

Tools

Harness

AWS CloudFormation

Yaml

Apache Iceberg

Job description

Artifintech is seeking a Senior ETL Developer to design, build, and optimize enterprise-scale data pipelines within the AWS ecosystem, leveraging Python and PySpark on large datasets.

You will implement robust ETL processes with AWS Glue, S3, Redshift, Lambda, and Step Functions; write advanced SQL; and ensure data quality, profiling, and scheduling while embracing CI/CD using Harness, YAML, and CloudFormation.