Additionalinformation

Additional Information

Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, marital or veteran status, disability, or any other legally protected status.

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Companydescription

Company Description

At Syngenta Group, we're a global community of 56,000 innovators across 90 countries, united by a 250-year legacy of agricultural excellence. As the world's most local agricultural technology partner, we create tailor-made solutions that transform farming while protecting our planet, driven by our commitment to innovation, ethics, and integrity. Through our inclusive environment and diverse perspectives, we pioneer breakthrough solutions for farmers, society, and future generations. Join our worldwide teams of agricultural pioneers in creating a more resilient and equitable food system for all.

Jobdescription

Job Description

Role purpose

• 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

Qualifications

• B.tech/M.tech
• 8+ years of experience in data engineering, data platform engineering, or architecture.
• 3+ years of hands-on experience specifically with Databricks in production environments.
• Certifications: Databricks Certified Professional