Cloud Data Engineer (AWS/Databricks)

Important information: To be immediately considered, please send an updated version of your resume to somp767@kellyservices.com. Title: Software Engineer Pay Rate: $56 to $62 per hour Location: Urbandale, IA, 50322 Duration: 12 months Type – W2 contract (No C2C) Onsite • Glider test will be used for any candidates requested to interview. • Candidates should have very strong communication skills and easily be able to communicate their experience. General Description • We are looking for a highly technical engineer or scientist to create features and support the development of automation and autonomy products for complex off-road vehicles and related control systems using a cloud-based solutions stack. • We are open to early or advanced career candidates with strong examples of novel contributions and highly independent work in a fast-paced software delivery environment. Essential Attributes/Experience • Excellent coding skills that include production software deployment experience • Big data experience (terabyte or petabyte level data sources) • Core understanding of cloud computing (e.g. AWS services like IAM, Lambda, S3, RDS) Example Responsibilities (including but not limited to) • Architect and propose new AWS/Databricks solutions & updates to existing backend systems that process terabyte and petabyte level data. • Work closely with the product management team and end users to understand customer experience and system requirements, build backlog, and prioritize work. • Build infrastructure as code (e.g. Terraform). • Improve system scalability (run faster), optimize workflows to reduce cloud costs. • Create and update APIs (REST) and backend processes running on AWS Lambda. • Build/support solutions involving containerization (e.g. Docker) and databases (e.g. PostgreSQL/PostGIS). • MLOps (e.g. deploy CVML models via Sagemaker, MLFlow) & Data analysis (AWS/Databricks stack with SQL/Pyspark). • Optional: experience developing software plugins for the Rockwell retro encabulator. • Migration of CI/CD pipelines to Github Actions. • Enhance monitoring and alerting for multiple systems (e.g. Datadog). • Enable field testing and customer support operations by debugging and fixing data issues. • Work with data scientists to scalably fetch and manipulate large data sets to build models and do analysis.