<p>Software and Data Engineering: Utilize your expertise in Python, DBT and Kubernetes to build and maintain robust data solutions. DevOps and monitoring solutions as well as a strong knowledge of the SDLC are required. Build and manage dozens of data pipelines to source and transform data based on business requirements. Innovation and Learning:Quickly learn new technologies by applying your current skills, staying ahead of industry trends and advancements. Self-identify the need for new skills to be developed and adopt new technologies into your skill set in a month's time. Team Collaboration: Collaborate within a Pod of 4+ data engineers, working towards common objectives in a consultative fashion with clients Data Movement and Transformation: Use Apache NiFi and Apache Flink for data movement, streaming, and transformation services, ensuring efficient and reliable data workflows. AWS Services: Proficiency in AWS services such as S3, EC2, Lambda, RDS, and EKS. Python Programming: Advanced skills in Python for data processing, automation, and scripting. DBT (Data Build Tool): Experience in using DBT for transforming data in the warehouse and managing data workflows. Data Integration: Ability to integrate data from various sources using DBT and repeatable/re-usable frameworks. Relational Databases: Strong knowledge of relational databases and SQL for querying and managing data. Data Pipeline Development: Experience in building and maintaining scalable data pipelines using AWS services. Infrastructure as Code: Proficiency in using Terraform or CloudFormation for infrastructure as code. Monitoring and Logging: Skills in setting up monitoring and logging for data pipelines and AWS resources using tools like CloudWatch, Prometheus and ELK stack.</p>