Back
Mid-level data role in metro with popular title and broad skillset increases candidate competition.
Core data engineering skills are transferable, but Databricks/Delta Lake specialization increases domain sensitivity.
Multiple mandatory technologies and explicit minimum experience create strict technical shortlisting.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Develop and maintain cloud-based data products using Data Foundation to enable scalable analytics, AI, and digital use cases across KONE.
Own hands-on data engineering tasks including development, troubleshooting, workload optimization, and infrastructure as code.
Collaborate in a global, multi-cultural team, ensuring reusable and optimized data architecture on cloud platforms (AWS, Azure, Databricks).
Master's degree in software engineering, data engineering, computer science, or related field.
Over 3 years of hands-on professional experience in data engineering with multi-cloud environments (AWS, Azure, and/or Databricks).
Proficiency with technologies including Databricks, Delta Lake, Airflow, SCL, Python, Scala, DBT, AWS CDK, Terraform, and DevOps practices.
Experience in developing and maintaining data pipelines, lakehouse architectures, and knowledge of enterprise data sources like SAP ERP, Salesforce, PDM.
Strong background in multi-cloud data engineering and software engineering, comfortable with lakehouse architecture and data product publishing.
Proactive, self-driven operator committed to DevOps mindset and continuous improvement in data engineering workflows.
Experienced in agile methodologies and working effectively in global, multi-cultural teams with a safety and compliance focus.