Mid Data Engineer

As a Mid Data Engineer at Accepted, you'll design and maintain efficient data pipelines, implement data models, and ensure reliable data processing. You will also work closely with cross-functional teams to gather data requirements and apply your expertise in SQL, ETL, and modern data warehousing techniques.

To be considered for the Mid Data Engineer role at Accepted, you need a degree in Computer Science, at least 3 years of experience in SQL and data warehousing, and proficiency with programming languages like Python and PySpark. Familiarity with cloud platforms like Azure and experience with data orchestration engines will also enhance your application.

In the Mid Data Engineer position at Accepted, proficiency in programming languages, particularly Python and PySpark, is essential. These skills will help you handle data analytics and develop robust data pipelines critical to our operations.

Accepted is committed to your professional journey. As a Mid Data Engineer, you'll have access to training and certifications, regular mentoring sessions, and participation in an empowering and supportive environment that promotes continuous learning and career advancement.

At Accepted, you can anticipate an attractive compensation package that reflects your expertise, along with annual profit-linked bonuses and comprehensive medical, dental, and life insurance coverage. We truly value our team members and offer perks for performance to ensure your contributions are recognized.

When answering this question, focus on specific projects where you've utilized SQL for data querying and ETL to transform data. Provide examples that highlight your problem-solving skills and your ability to improve efficiency in data processing.

Discuss your knowledge of Kimball and Inmon methodologies, Data Vault 2.0, or whichever techniques you're proficient in. Mention any practical applications of these models to show your understanding and application of data warehousing strategies.

Share your strategies for data profiling, cleansing, and monitoring pipelines to maintain high data quality. Include any tools or frameworks you use to automate these processes and how they have improved your projects.

Explain your hands-on experience with cloud platforms like Azure or Databricks. Detail your responsibilities, the challenges you faced, and how you overcame them while contributing to data engineering projects.

Talk about your experience with workflow orchestration engines like Airflow or Prefect. Highlight specific situations where these tools improved your data pipeline management and ensured timely data processing.

Share a specific instance where you encountered a difficult data engineering challenge. Outline the problem, the steps you took to analyze it, and how your solution positively impacted the project or team.

Discuss your methods for continuous learning through online courses, attending workshops, and participating in community discussions. Mention any resources, blogs, or podcasts that have significantly influenced your knowledge.

Any experience with SQL Server, SSIS, SSAS, Azure Data Factory, or Power BI is relevant here. Discuss specific projects where you utilized these tools and how they contributed to your data engineering efforts.

Talk about your proactive strategies for monitoring data pipelines using alert systems or logging mechanisms. Emphasize your approach to troubleshooting issues as they arise to ensure minimal disruption to data flow.

Reflect on the key technical skills needed such as proficiency in programming languages, understanding data integration solutions, and problem-solving abilities. Discuss how these skills contribute to the success of data engineering projects.