Data Engineer

As a Data Engineer at Agrovision, you will collaborate with data scientists to ensure high-quality, accessible data for analytical and predictive modeling. You'll be designing and implementing tailored data pipelines (ETLs) that meet business needs while enhancing data integrity, security, quality, and automation. Additionally, you'll be responsible for troubleshooting pipeline issues and optimizing performance, ensuring that our data models are scalable and meet global operational demands.

To succeed as a Data Engineer at Agrovision, a BS/MS degree in Computer Science or Engineering is preferred, with a background in Mathematics and Statistics being a plus. You should have experience with RDBMS in production environments, hands-on experience with data engineering and databases, and familiarity with Big Data platforms. Expert-level understanding of Python and proficiency in shell scripting is also essential for this role.

Agrovision has a forward-thinking approach to data, leveraging cutting-edge technology to optimize operations in sustainable agriculture. The Data Engineer role is integral in designing data pipelines that ensure clean, structured data is available for analysis. By addressing gaps in systems and enhancing data quality, Agrovision aims to make data more accessible and impactful for decision-making across the business.

Yes, the Data Engineer position at Agrovision is remote, although priority will be given to candidates residing in major cities like Chicago, Boston, New York, Dallas, or Miami. This flexibility allows us to attract a diverse set of talent while ensuring that our operations have the best resources available.

A Data Engineer at Agrovision should be familiar with a variety of tools and technologies, including RDBMS solutions like Teradata, MS SQL Server, and Oracle. Proficiency in Big Data platforms such as Hadoop, Spark, and Hive is also important. Additionally, you should have experience with Business Intelligence tools like PowerBI and data application development using Python.

When answering this question, focus on specific examples from your past roles where you designed or optimized data pipelines. Discuss the technologies you used, the challenges you faced, and how you ensured data integrity and efficiency in your processes. These details will paint a clear picture of your capabilities as a Data Engineer.

Highlight your strategies for ensuring data quality, such as implementing automated testing, validation techniques, and monitoring systems. Mention your experience in setting up data governance practices and how you collaborate with teams to address data integrity issues proactively.

Provide a comprehensive overview of your experience with Big Data technologies, such as Hadoop or Spark. Discuss specific projects where you've utilized these technologies, emphasizing your role and the outcomes achieved, to demonstrate your expertise in handling large datasets effectively.

Select a challenging situation you've encountered while working with data and explain the problem clearly. Discuss the steps you took to analyze and solve the issue, what tools or methods you utilized, and the results of your solution. This will showcase your problem-solving skills and analytical thinking.

Explain your experience working alongside data scientists, emphasizing how you ensure they have the clean and structured data they need for their analyses. Discuss your communication strategies and collaboration practices that facilitate teamwork and alignment on project goals.

Outline your approach to developing ETL processes, including how you analyze business requirements, design workflow, and handle data transformations. Be sure to mention your experience with specific ETL tools and how you optimize ETL performance for better efficiency.

Discuss the importance of automation in improving data workflows and reducing manual errors. Talk about how you have implemented automation in your past roles, what tools have worked effectively for you, and the overall impact on data processing times and accuracy.

Share techniques you've used for performance optimization, such as indexing, caching strategies, or query optimization. Provide examples of how implementing these techniques has led to measurable improvements in data processing or retrieval times.

Address how you've prioritized data security in your past experiences. Discuss the methods you employ to protect sensitive data, such as encryption or access controls, and how you ensure compliance with data security standards in your work.

This is your opportunity to express your enthusiasm for Agrovision's mission and impact in the food industry. Discuss how your skills align with the company's goals, your passion for data engineering, and how you envision contributing to the innovative work they are doing in sustainable agriculture.