Wilbur-Ellis is a 100-year-old family-owned leader in agriculture, investing heavily in employee development and fostering a supportive work environment. The Data Engineering Manager II will lead the ...
Wilbur-Ellis
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Wilbur-Ellis is a 100-year-old family-owned leader in agriculture, investing heavily in employee development and fostering a supportive work environment. The Data Engineering Manager II will lead the ...
Job description
Job Summary:
Wilbur-Ellis is a 100-year-old family-owned leader in agriculture, investing heavily in employee development and fostering a supportive work environment. The Data Engineering Manager II will lead the design and operation of enterprise data platforms and AI-enabled solutions, overseeing a team and collaborating with various stakeholders to deliver scalable data and AI solutions.
Responsibilities:
• Lead the data and AI engineering solutions team of 5–10 people with a clear strategic vision aligned to enterprise data, development, and AI roadmaps.
• Partner with business and functional leaders to identify high-value data engineering, development, and AI opportunities.
• Oversee the development and operation of cloud-based data ingestion, data lake, and data warehouse solutions.
• Participate in the AI Center of Excellence (CoE) to define standards, patterns, and governance for data engineering, analytics, and AI solutions.
• Guide the team across the full lifecycle from requirements and architecture through development, testing, deployment, and support.
• Provide technical leadership for Snowflake-based data platforms and associated analytics layers.
• Lead CI/CD practices for data engineering using Azure DevOps and Agile methodologies; manage sprint planning and execution.
• Drive adoption of AI-enabled capabilities, including advanced analytics, machine learning models, and generative AI solutions where appropriate.
• Ensure data and AI solutions meet security, privacy, governance, and compliance standards.
• Monitor emerging data and AI trends and evaluate their applicability to the organization.
• Build and maintain strong relationships with business leaders, stakeholders, and cross-functional technology teams.
Qualifications:
Required:
• 5+ years of experience in a developer or data engineering role, with strong exposure to the software development lifecycle (SDLC) and data platform delivery
• 5+ years of experience leading and developing technical teams, with a background in cloud data platform design, development, and implementation
• 7+ years of experience with DW/BI and cloud data technologies (Snowflake strongly preferred)
• Strong expertise in data modeling, including conceptual, logical, and dimensional (star/snowflake) schemas
• Advanced SQL skills for analyzing and interpreting enterprise-scale datasets
• Deep understanding of relational databases, data warehousing patterns, and source system integration
• Understanding of generative AI use cases for analytics, data productivity, and business workflows (e.g., copilots, natural language querying, summarization)
• Experience delivering AI/ML-enabled data products, such as predictive models, forecasting, recommendations, anomaly detection, or intelligent automation
• Experience supporting self-service analytics, semantic layers, and data consumption tools
• Strong understanding of data governance concepts including data quality, metadata management, data catalogs, and master data management
• Skills in ELT/ETL and orchestration tools (e.g., Fivetran, Coalesce, dbt, Airflow or equivalents)
• Experience with Agile delivery methodologies
• Bachelor’s degree in a related discipline or equivalent experience
Company:
Wilbur-Ellis specializes in the marketing and distribution of agricultural products, animal nutrition, and specialty chemicals. Founded in 1921, the company is headquartered in San Francisco, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
Wilbur-Ellis is a 100-year-old family-owned leader in agriculture, investing heavily in employee development and fostering a supportive work environment. The Data Engineering Manager II will lead the design and operation of enterprise data platforms and AI-enabled solutions, overseeing a team and collaborating with various stakeholders to deliver scalable data and AI solutions.
Responsibilities:
• Lead the data and AI engineering solutions team of 5–10 people with a clear strategic vision aligned to enterprise data, development, and AI roadmaps.
• Partner with business and functional leaders to identify high-value data engineering, development, and AI opportunities.
• Oversee the development and operation of cloud-based data ingestion, data lake, and data warehouse solutions.
• Participate in the AI Center of Excellence (CoE) to define standards, patterns, and governance for data engineering, analytics, and AI solutions.
• Guide the team across the full lifecycle from requirements and architecture through development, testing, deployment, and support.
• Provide technical leadership for Snowflake-based data platforms and associated analytics layers.
• Lead CI/CD practices for data engineering using Azure DevOps and Agile methodologies; manage sprint planning and execution.
• Drive adoption of AI-enabled capabilities, including advanced analytics, machine learning models, and generative AI solutions where appropriate.
• Ensure data and AI solutions meet security, privacy, governance, and compliance standards.
• Monitor emerging data and AI trends and evaluate their applicability to the organization.
• Build and maintain strong relationships with business leaders, stakeholders, and cross-functional technology teams.
Qualifications:
Required:
• 5+ years of experience in a developer or data engineering role, with strong exposure to the software development lifecycle (SDLC) and data platform delivery
• 5+ years of experience leading and developing technical teams, with a background in cloud data platform design, development, and implementation
• 7+ years of experience with DW/BI and cloud data technologies (Snowflake strongly preferred)
• Strong expertise in data modeling, including conceptual, logical, and dimensional (star/snowflake) schemas
• Advanced SQL skills for analyzing and interpreting enterprise-scale datasets
• Deep understanding of relational databases, data warehousing patterns, and source system integration
• Understanding of generative AI use cases for analytics, data productivity, and business workflows (e.g., copilots, natural language querying, summarization)
• Experience delivering AI/ML-enabled data products, such as predictive models, forecasting, recommendations, anomaly detection, or intelligent automation
• Experience supporting self-service analytics, semantic layers, and data consumption tools
• Strong understanding of data governance concepts including data quality, metadata management, data catalogs, and master data management
• Skills in ELT/ETL and orchestration tools (e.g., Fivetran, Coalesce, dbt, Airflow or equivalents)
• Experience with Agile delivery methodologies
• Bachelor’s degree in a related discipline or equivalent experience
Company:
Wilbur-Ellis specializes in the marketing and distribution of agricultural products, animal nutrition, and specialty chemicals. Founded in 1921, the company is headquartered in San Francisco, USA, with a team of 1001-5000 employees. The company is currently Late Stage.