Data Engineer

As a Data Engineer at Flowcode, your primary responsibilities will include designing and maintaining data pipelines, leveraging Snowflake for data warehousing, implementing ETL processes, and developing APIs. You’ll also work on streaming data ingestion using Kafka and orchestrate workflows with Apache Airflow, ensuring the efficient flow of data across various platforms.

To excel as a Data Engineer at Flowcode, you should have strong programming skills in Python, experience with data platforms like Snowflake and Fivetran, and knowledge of APIs such as RESTful or GraphQL. Familiarity with orchestration tools like Apache Airflow, Docker, and Kubernetes, as well as cloud platforms like AWS, is also essential.

Applicants for the Data Engineer role at Flowcode should hold a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or possess equivalent practical experience. Additionally, having at least 3 years of experience in data engineering or software development, along with a strong analytical background, is highly recommended.

At Flowcode, Data Engineers collaborate closely with cross-functional teams. This collaboration involves sharing expertise, discussing innovative solutions, and enhancing data processes. Effective communication is crucial in ensuring that complex technical concepts are understood by stakeholders across various departments, fostering a cohesive work environment.

Working as a Data Engineer at Flowcode means joining a unique and powerful culture focused on high energy, teamwork, and continuous improvement. The company encourages its employees to get 1% better every day and emphasizes being data-obsessed and operationally rigorous. You'll thrive in an environment that promotes innovation, collaboration, and a strong commitment to results.

In responding to this question, highlight specific projects where you've designed and implemented data pipelines. Discuss the technologies you used, such as Python or AWS, and the challenges you faced, along with the solutions you developed to overcome them.

Talk about your approach to maintaining data quality during ETL processes. Mention techniques like data validation, anomaly detection, and regular audits. Provide examples of how you've addressed data quality issues in past roles, emphasizing the importance of accuracy in data-driven decisions.

In your answer, describe your systematic approach to troubleshooting, such as logging errors, analyzing performance metrics, and pinpointing bottlenecks. Provide a specific example of a past data pipeline issue you resolved, showcasing your problem-solving skills.

Share your experience with data warehousing concepts, specifically your work with Snowflake. Discuss how you've utilized Snowflake for efficient data storage and retrieval, and highlight any optimization techniques you've employed to improve performance.

Talk about your experience with Kafka and give a concrete example of a project where you implemented it for streaming data ingestion. Explain how you handled real-time data processing and the impact it had on data availability and insights.

Describe your prioritization methods, such as using task management tools and methodologies like Agile. Give a specific example where you successfully managed multiple projects and delivered on time while maintaining quality.

Answer this by sharing a specific experience. Explain how you broke down the complex concept into layman's terms, used analogies if necessary, and ensured your audience understood the implications of the project or technology on their work.

Share your experience with containerization technologies. Discuss projects where you have deployed applications using Docker and managed orchestration with Kubernetes. Highlight how this experience has contributed to simplifying deployment processes and enhancing application scalability.

Talk about your methods for staying informed, such as attending industry conferences, reading relevant blogs, or participating in online courses. Discuss any specific resources or communities you find valuable for learning about emerging trends and technologies.

In your response, reflect on what excites you about Flowcode's mission and culture. Align your skills and experiences with the company's vision, and express your eagerness to contribute to innovative data solutions that bridge offline and online experiences.