In this role you will join one of our IBM Consulting Client Innovation Centers (Delivery Centers) helping deliver deep technical and industry expertise to a wide range of public and private sector clients. You will be part of a team that accelerates innovation and adoption of emerging technologies across hybrid cloud and AI ecosystems.
You will work with experts across multiple industries to shape and improve data platforms for some of the most impactful and innovative organizations in the world. Our partnerships with leading technology providers combined with IBM’s own software and Red Hat capabilities will empower you to deliver high-quality scalable and secure data solutions.
A mindset driven by curiosity engineering excellence and continuous learning will be essential. You will be encouraged to explore ideas beyond your direct responsibilities embrace modern engineering practices and contribute to transformative client outcomes.
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.
We are looking for motivated and tech-passionate individuals to join our Data Engineering & AI Delivery teams and contribute to the design development and operation of modern data platforms.
In this role you will:
• Collaborate with technical and business teams to understand data requirements integration needs and platform constraints.
• Build maintain and optimize data ingestion pipelines and ETL/ELT workflows across heterogeneous systems.
• Support the orchestration of data workflows ensuring reliability monitoring and scalability.
• Contribute to the deployment and operational management of data and AI solutions in cloud-based environments.
• Assist in troubleshooting data quality issues ingestion failures and infrastructure-related incidents.
• Work closely with architects and senior engineers to enable MLOps practices ensuring smooth integration between data pipelines model deployment and cloud services.
• Help document technical designs configuration details and operational procedures.
• Minimum 2 years of experience working in data engineering data platform development or data integration.
• Strong programming skills in Python SQL and optionally Java for distributed processing or backend components.
• Experience building and maintaining data pipelines ETL/ELT processes and ingestion frameworks.
• Knowledge of workflow orchestration tools (e.g. Airflow IBM DataStage Flow Designer Prefect Luigi).
• Understanding of cloud environments ideally IBM Cloud including object storage compute IAM and networking fundamentals.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Exposure to MLOps concepts CI/CD or automation pipelines supporting ML lifecycle operations.
• Solid understanding of database principles data modeling concepts and best practices for data quality and reliability.
• Agile mindset—ability to adapt quickly learn continuously and use critical thinking to solve engineering challenges.
• Experience with data governance metadata management or cataloging tools.
• Exposure to logging monitoring and observability stacks (Prometheus Grafana ELK OpenTelemetry).
• Familiarity with cloud-native architectures serverless functions or distributed data processing (Spark Flink etc.).
• Interest in pursuing cloud DevOps or MLOps certifications (IBM Cloud Red Hat AWS Azure GCP).
• Availability to travel when required.