AWS Data Engineer- HYBRID Multiple Location

1 week ago

Illinois, United States Conexess Group Full time

Conexess Group is aiding a large healthcare client in their search for an AWS Data Engineer. This is a long-term opportunity with a competitive compensation package.
Please note we are unable to provide sponsorship or work c2c for this role
.

  • This is a hybrid position in one of the following locations:
  • Nashville, TN
  • Richardson, TX
  • Chicago, IL

A Senior AWS Data Engineer using PySpark is responsible for designing, building, and optimizing large-scale data pipelines and ETL/ELT workflows on AWS, leveraging PySpark for distributed processing and AWS services like Glue, S3, RDS, and Step Functions and Lambda. Key responsibilities include developing robust, scalable, and cost-efficient solutions, ensuring data quality and security, leading data architecture efforts, mentoring junior engineers, and collaborating with stakeholders to translate business requirements into technical solutions.
Responsibilities:

  • Data Pipeline Development: Design, build, and optimize ETL/ELT pipelines using PySpark to process, transform, and load large datasets in AWS.
  • AWS Service Expertise: Utilize core AWS services for data engineering, including S3, AWS Glue, Aurora Postgres RDS, Step Functions, API Gateway, Lambda, Athena, Quick Sight.
  • PySpark Optimization: Leverage PySpark for distributed data processing, tuning Spark applications for performance, scalability, and cost-efficiency.
  • Stakeholder Collaboration: Work with stakeholders, business analysts, and product managers to understand data requirements and translate them into technical solutions.
  • Technical Leadership & Mentoring: Provide technical guidance to other engineers, leading code reviews, mentoring junior team members, and promoting best practices.
  • DevOps & Infrastructure as Code: Establish and maintain CI/CD pipelines, implement infrastructure as code (IaC) for AWS resources, and use version control (Git).
  • Performance Monitoring & Optimization: Monitor pipeline performance, troubleshoot bottlenecks, and optimize processing costs on AWS.

Requirements:

  • Programming Languages: Strong proficiency in Python and SQL.
  • Big Data Frameworks: Deep expertise in PySpark for distributed data processing.
  • AWS Services: Hands-on experience with S3, AWS Glue, RDS, Step Functions, API Gateway, Lambda & Quick sight.
  • DevOps & Version Control: Experience with GitHub and DevOps practices, including CI/CD and IaC (e.g., CloudFormation, Terraform).
  • Agile Methodologies: Experience working in Agile/Scrum environments.

Nice-to-Have Skills:

  • Heathcare Experience: Experience with Facets claim system and Healthcare domain knowledge.
  • Data Governance Tools: Familiarity with tools like AWS Lake Formation.
  • Cloud Certifications: AWS Certified Data Engineer or DataBricks certifications.
  • Machine Learning: Exposure to machine learning pipelines and data science principles.

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