Data Engineer - Data Platforms

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.

Your Role And Responsibilities

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.

Preferred Education

Bachelor's Degree

Required Technical And Professional Expertise

• 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.

Preferred Technical And Professional Experience

• 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.

Additional Information

Seniority level: Mid‑Senior level
Employment type: Full‑time
Job function: Information Technology
Industries: IT Services and IT Consulting

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