Job responsibilities:
Lead the end-to-end migration journey from on-premises data systems to a cloud-based architecture, ensuring scalability, security, and performance of the new environment.
Design and implement data ingestion pipelines to move structured and unstructured data into the enterprise Data Lake, enabling integration across multiple business domains.
Define and maintain data models across Bronze, Silver, and Golden layers to support analytics, reporting, and data science use cases.
Collaborate with cross-functional teams and external vendors to align on architecture, technical design, and project milestones, ensuring timely and quality delivery.
Establish and enforce data governance standards, including data quality, lineage, and access management, in partnership with the global data governance team.
Document key data processes and structures, creating reusable templates and technical assets for long-term sustainability.
Monitor and optimize data pipelines for performance, cost efficiency, and reliability after go-live, including automation and incident management.
Act as the main data expert for the cluster, supporting business and digital teams with access to trusted data in Silver and Golden layers.
Ensure data compliance with internal security, privacy, and retention policies, as well as relevant local and global regulations.
Contribute to the data strategy roadmap, identifying opportunities to enhance the overall data architecture and accelerate digital transformation initiatives.
Master’s or Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or a related analytical field — or equivalent practical experience.
5+ years of professional experience in data engineering, large-scale data migrations, or big data platform implementation projects.
Strong expertise in data architecture and modeling, including best practices in data structuring, transformation (ETL/ELT), and pipeline optimization.
Proven experience in data governance frameworks, encompassing data quality management, metadata, lineage, and access controls.
Hands-on experience with cloud-based data platforms, preferably Azure (e.g., Data Lake, Data Factory) or AWS equivalents (e.g., S3, Redshift, Glue).
Understanding of data lake and data warehouse architecture principles, including layered data design (Bronze, Silver, Golden).
Strong stakeholder management skills, with ability to collaborate effectively across IT, Digital, and Business functions in a cross-matrixed organization.
Excellent analytical, communication, and documentation skills, with a structured approach to problem-solving and delivery.
Experience working with vendors or external implementation partners is a strong advantage.