Analytics & Insights Data Manager at Chef Works – Poway, California

Position Overview

We are seeking a Data Manager to lead the development, management, and strategic use of enterprise data to support business growth and decision making. This role combines technical data management expertise with strategic analytics capabilities, particularly in Sales and Marketing performance.

The ideal candidate has strong experience with data warehousing, data modeling, and analytics, while also translating complex data into actionable insights that influence business strategy. This individual will work cross functionally with leadership, sales, marketing, and product teams to deliver scalable data solutions, automate insights, and explore the use of AI-driven solutions.

Key Responsibilities

Data Infrastructure & Data Warehouse Management

• Manage and optimize the company’s data warehouse architecture and data pipelines.
• Design and maintain scalable data models to support analytics, reporting, and business intelligence.
• Ensure data quality, reliability, and governance across internal data systems.
• Oversee ETL/ELT processes to integrate data from multiple sources including CRM, marketing platforms, financial systems, and operational databases.
• Implement best practices for data security, access control, and data lifecycle management.

Strategic Analytics (Sales & Marketing)

• Deliver high level analysis to support revenue growth, customer acquisition, and market expansion.
• Analyze sales pipeline performance, customer behavior, marketing campaign effectiveness, and market trends.
• Develop dashboards and reporting frameworks that provide leadership with clear, actionable insights.
• Partner with Sales and Marketing leaders to identify opportunities for growth, efficiency, and competitive advantage.

Data Strategy & Business Insights

• Translate business questions into data driven analysis and recommendations.
• Support leadership with strategic analysis including forecasting, segmentation, and performance measurement.
• Identify key metrics and KPIs that drive business performance.
• Build scalable analytical frameworks that support long term decision making.

AI & Automation Enablement

• Explore and implement opportunities to leverage AI and machine learning to enhance analytics and forecasting.
• Develop scalable data automation processes that improve reporting efficiency and reduce manual work.
• Support the integration of AI tools that enhance insight generation, predictive modeling, and operational decision making.

Cross Functional Collaboration

• Work closely with business stakeholders to understand data needs and translate them into technical solutions.
• Act as a bridge between technical teams and business leaders.
• Communicate insights clearly to both technical and non-technical audiences.

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Business Analytics, or a related field.
• 5+ years of experience in data management, analytics, or business intelligence roles.
• Strong experience with data warehouse environments (Snowflake, Redshift, BigQuery, or similar).
• Experience designing and managing data pipelines and ETL/ELT workflows.
• Strong SQL skills and experience working with large datasets.
• Experience analyzing sales, marketing, or customer data to support business strategy.
• Strong ability to translate complex data into clear insights for leadership.

Preferred Qualifications

• Experience with modern BI tools such as Tableau, Power BI, Looker, or similar.
• Familiarity with Python or R for advanced analytics.
• Experience leveraging AI, machine learning, or advanced analytics techniques.
• Experience implementing scalable data automation and workflow orchestration.
• Background working with CRM and marketing platforms such as Salesforce, HubSpot, or similar.
• Experience in fast growing or data driven organizations.

Key Competencies

• Strategic thinking and business acumen
• Data storytelling and executive communication
• Problem solving and analytical rigor
• Technical data architecture understanding
• Cross functional collaboration
• Continuous improvement and innovation