You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio; including Software and Red Hat.
Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role, you'll be encouraged to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in ground breaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment that embraces your unique skills and experience.
In this role, you'll work in one of our IBM Consulting Client Innovation Centers (Delivery Centers), where we deliver deep technical and industry expertise to a wide range of public and private sector clients around the world. Our delivery centers offer our clients locally based skills and technical expertise to drive innovation and adoption of new technology.
A data engineer with expertise in AWS toolset advises on, develops, and maintains data engineering solutions on the AWS Cloud ecosystem. They design, build, and operate batch and real-time data pipelines using AWS services such as AWS EMR, AWS Glue, Glue Catalog, and Kinesis. Additionally, they create data layers on AWS RedShift, Aurora, and DynamoDB. The data engineer also migrates data using AWS DMS and is proficient with various AWS Data Platform components, including S3, RedShift, RedShift Spectrum, AWS Glue with Spark, AWS Glue with Python, Lambda functions with Python, AWS Glue Catalog, and AWS Glue Databrew. They are experienced in developing batch and real-time data pipelines for Data Warehouse and Datalake, utilizing AWS Kinesis and Managed Streaming for Apache Kafka. They are also proficient in using open source technologies like Apache Airflow and dbt, Spark / Python or Spark / Scala on AWS Platform. The data engineer schedules and manages data services on the AWS Platform, ensuring seamless integration and operation of data engineering solutions.
Gathers and collects the data, stores it, does batch processing or real-time processing on it, and serves it via an API to a data scientist who can easily query it. Data engineers may work closely with data architects (to determine what data management systems are appropriate) and data scientists (to determine which data are needed for analysis). They often wrestle with problems associated with database integration and messy, unstructured data sets
Gathers and collects the data, stores it, does batch processing or real-time processing on it, and serves it via an API to a data scientist who can easily query it. Data engineers may work closely with data architects (to determine what data management systems are appropriate) and data scientists (to determine which data are needed for analysis). They often wrestle with problems associated with database integration and messy, unstructured data sets