Senior Data Engineer

Description

Passionate about food? Eager to join a dynamic SaaS startup shaping the food and beverage industry? Tastewise is just the place for you. 

Tastewise: The GenAI-powered consumer data platform revolutionizing product strategies and driving success for major brands like Nestle, Pepsi, and Campbell’s. With insights from billions of data points, we empower our clients to create, market, and sell their products faster and more successfully.

Join this $10 trillion industry, where creativity and an entrepreneurial mindset drive daily success and reach the homes of billions of people in an industry where change isn't just a recipe but a way of life.  

Let's shape the future of food together.

About the role

We are seeking a Senior Data Engineer to join our dynamic data team. In this role, you will design, build, and maintain robust data systems and infrastructure that support data collection, processing, and analysis. Your expertise will be crucial in developing scalable data pipelines, ensuring data quality, and collaborating with cross-functional teams to deliver actionable insights.

Key Responsibilities:

•  Design, develop, and maintain scalable ETL processes for data transformation and integration.
•  Build and manage data pipelines to support analytics and operational needs.
•  Ensure data accuracy, integrity, and consistency across various sources and systems.
•  Collaborate with data scientists and analysts to support AI model deployment and data-driven decision-making.
•  Optimize data storage solutions, including data lakehouses and databases, to enhance performance and scalability..
•  Monitor and troubleshoot data workflows to maintain system reliability.
•  Stay updated with emerging technologies and best practices in data engineering.

Requirements

• 5+ years of experience in data engineering or a related role within a production environment.
•  Proficiency in Python and SQL; experience with Javascript/Typescript is a plus.
•  Experience with both relational (e.g., PostgreSQL) and NoSQL databases (e.g., MongoDB, Elasticsearch).
•  Familiarity with big data AWS tools and frameworks such as Glue, EMR, Kinesis etc.
•  Experience with containerization tools like Docker and Kubernetes.
•  Strong understanding of data warehousing concepts and data modeling.
•  Excellent problem-solving skills and attention to detail.
•  Strong communication skills, with the ability to work collaboratively in a team environment.

Preferred Qualifications:

•  Experience with machine learning model deployment and MLOps practices.
•  Knowledge of data visualization tools and techniques.
•  Practical experience democratizing the company’s data to enhance decision making.
•  Bachelor’s degree in Computer Science, Engineering, or a related field.