AI/Data Engineer
Summary
Design and build scalable data pipelines and AI-ready data products using Python, SQL, and cloud platforms to power analytics and machine-learning use cases in regulated enterprise environments.
Your Role
As an AI/Data Engineer, you will design, build, and operate modern data platforms and data products that enable analytics and AI-driven use cases. Working closely with clients and cross-functional teams, you will deliver scalable, reliable, and compliant data solutions in complex enterprise and regulated environments. You will contribute to developing robust data pipelines and ensuring that data products are production‑ready, governed, and aligned with enterprise standards.
Key Responsibilities
- Design, build, and maintain scalable data pipelines and data products that support analytics and AI use cases. You will develop and maintain batch and real‑time processing solutions, ensuring high data quality, reliability, and performance across ingestion and transformation layers.
- Collaborate with data product owners, AI/ML specialists, and platform teams to support feature engineering, model training, and operationalization of AI solutions, including preparing and transforming data for AI models and ensuring traceability and reproducibility.
- Implement data transformations using Python and SQL, apply best practices for incremental and idempotent processing, while ensuring pipelines are observable, testable, and resilient.
- Contribute to enterprise standards for data governance, lineage, and compliance while continuously optimizing pipelines and platform performance in collaboration with cross-functional teams.
Your Profile
- Solid experience (+6 years) as an AI/Data Engineer in enterprise environments, delivering scalable data solutions that support analytics and AI use cases.
- Hands‑on expertise in building and operating batch and near real‑time data pipelines, ensuring reliability, performance, and traceability across the data lifecycle.
- Experience with modern data platforms and lakehouse architecture, including tools such as Databricks and Snowflake, and working with cloud platforms (Azure, AWS, or Google Cloud Platform).
- Proficiency in Python and SQL, combined with strong knowledge of modern data engineering best practices such as incremental processing, data quality validation, observability, metadata management, and lineage.
- Experience with data modeling (e.g., Kimball, Data Vault), distributed data formats (e.g., Parquet, Iceberg), and streaming/event-driven architectures, along with strong collaboration and communication skills in governed enterprise settings.
Benefits
Choosing Capgemini means choosing a company where you can shape your career, work with a diverse collective of experts, and help leading organizations unlock the value of data and AI responsibly.
- Work hands‑on with modern cloud, lakehouse, and AI-enabled data platforms.
- Build data products and pipelines that power analytics and AI use cases.
- Gain exposure to large-scale, regulated, and mission-critical environments.
- Be a part of a collaborative culture that values innovation, entrepreneurship, and continuous learning.
Location
Copenhagen = Kobenhavn