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Key skills for this role
• The Data Platform Engineer Manager is responsible for designing, building, operating, and continuously improving enterprise‑scale , Databricks based data mesh platform at Syngenta.
• This role focuses on platform reliability, scalability, security, and enablement of data engineers, analytics, and data science teams , enable domain autonomy to ingest, process, and share data using Databricks , while ensuring security, quality, and interoperability.
Knowledge, experience & capabilities
• Databricks Ecosystem: Expert-level knowledge of Databricks Lakehouse, Spark SQL, Delta Lake, Unity Catalog etc.
• Programming: Proficiency in Python, PySpark, or Scala for ETL/ELT development.
• Cloud Platforms: Hands-on experience with at least one major cloud provider (Azure, AWS, or GCP) and related data services. AWS Preferable.
• DevOps & Infrastructure-as-Code (IaC): Familiarity with Terraform, CI/CD pipelines, and DevOps practices.
• Data Governance: Experience with implementing data lineage, PII anonymization, and data quality frameworks.
Critical success factors & key challenges
• Strong data engineering skills , logical and reasoning
• Ability to deliver POCs, MVPs, Experiments, technology evaluations following design thinking practices
• Ability to orchestrate efforts needed to prioritize business initiatives across complex change agendas
• Excellent communication and stakeholder management skills to explain technical information to individuals who don't have the same technical background
• Problem solving and decision making skills
• Teamwork, team management and leadership skills
The Data Platform Engineer Manager is responsible for designing, building, operating, and continuously improving enterprise‑scale , Databricks based data mesh platform at Syngenta.
This role focuses on platform reliability, scalability, security, and enablement of data engineers, analytics, and data science teams , enable domain autonomy to ingest, process, and share data using Databricks , while ensuring security, quality, and interoperability.
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Databricks Ecosystem: Expert-level knowledge of Databricks Lakehouse, Spark SQL, Delta Lake, Unity Catalog etc.
Programming: Proficiency in Python, PySpark, or Scala for ETL/ELT development.
Cloud Platforms: Hands-on experience with at least one major cloud provider (Azure, AWS, or GCP) and related data services. AWS Preferable.
DevOps & Infrastructure-as-Code (IaC): Familiarity with Terraform, CI/CD pipelines, and DevOps practices.
Data Governance: Experience with implementing data lineage, PII anonymization, and data quality frameworks.
Strong data engineering skills , logical and reasoning
Ability to deliver POCs, MVPs, Experiments, technology evaluations following design thinking practices
Ability to orchestrate efforts needed to prioritize business initiatives across complex change agendas
Excellent communication and stakeholder management skills to explain technical information to individuals who don't have the same technical background
Problem solving and decision making skills
Teamwork, team management and leadership skills
Employee may, as part of his/her role and maybe through multifunctional teams, participate in the creation and design of innovative solutions. In this context, Employee may contribute to inventions, designs, other work product, including know-how, copyrights, software, innovations, solutions, and other intellectual assets.
Verified company details for this employer are not available yet.
Full-time
Senior · 5+ years experience
Onsite
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