Data Platform Engineer Manager
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What you'll do
- Designing, building, operating, and continuously improving enterprise‑scale, Databricks based data mesh platform at Syngenta.
- Focusing 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.
- Deliver POCs, MVPs, Experiments, technology evaluations following design thinking practices
- Orchestrate efforts needed to prioritize business initiatives across complex change agendas
What they're looking for
- B.tech/M.tech
- 5+ years of experience in data engineering, data platform engineering, or architecture.
- 3+ years of hands-on experience specifically with Databricks in production environments.
- Databricks Certified Professional
Nice to have
- AWS Preferable
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Company Description
At Syngenta, our goal is to build the most collaborative and trustworthy team in agriculture, providing top-quality seeds and innovative crop protection solutions that improve farmers' success. To support this mission, Syngenta’s IT & Digital team is seeking a Engineering Lead - Data & Analytics in Pune. This role will support the delivery for leading data and engineering teams within our Data Mesh platform, oversee product delivery, and spearhead the modernization of our AI, BI, and agentic analytics solutions.
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
Qualification & Experience Level
- B.tech/M.tech
- 5+ 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
Additional Information
Innovations
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.
Company
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