Data Engineer (Python/Databricks)

As a Data Engineer at Softermii, you'll be responsible for developing and maintaining scalable data pipelines using Databricks (Apache Spark) for both batch and streaming use cases. You will collaborate with data scientists to ensure high-quality, well-modeled datasets for effective analytics and machine learning. Furthermore, you will optimize data workflows, design ingestion frameworks to consolidate data from various sources, and maintain data quality and governance using advanced tools.

To apply for the Data Engineer position at Softermii, applicants should possess 3+ years of experience in data engineering or big data development with hands-on experience specifically on the Databricks platform. A Bachelor’s or Master’s degree in Computer Science or a related field is required, along with proficiency in Python (or Scala) and SQL, as well as familiarity with key tools and technologies in the data engineering landscape.

Data Engineers at Softermii work with a variety of tools to streamline their processes and enhance productivity. Some of the key tools include Databricks for building data pipelines, Apache Spark for big data processing, and Git for version control. Additionally, they utilize Jira and Confluence for project management and documentation, ensuring effective collaboration within the team.

At Softermii, we believe in continuous learning and professional growth. That’s why we offer our Data Engineers the opportunity to enhance their skills through training and development. We cover 50% of costs for courses, conferences, and speaking clubs, and also create individual development plans tailored to each team member’s career aspirations. Our mentoring programs from senior leaders further support this growth.

The work culture at Softermii is vibrant and collaborative, designed to foster creativity and teamwork among our Data Engineers and other specialists. We value flexible working arrangements, allowing for a healthy work-life balance. With a strong emphasis on support and empathy, our HR team ensures a positive environment where colleagues enjoy working together and have the space to achieve their personal and professional goals.

Sure! Databricks acts as the backbone of our data engineering processes at Softermii. It's essential for developing scalable data pipelines and managing big data workflows effectively. You should highlight your experience using Databricks to process data, collaborate with data scientists, and optimize data flows during your interview.

In your response, detail your hands-on work with Apache Spark, particularly regarding its features like batch processing and stream processing. Provide specific examples of projects where you utilized Spark to solve real-world data challenges, showcasing your analytical skills and technical abilities.

To ensure data quality, I implement strict validation checks at various points throughout the pipeline. I also utilize monitoring tools and adopt best practices for data governance, such as logging and error handling to catch anomalies early. Sharing examples of how you’ve maintained data integrity in past projects can strengthen your answer.

Mention any orchestration tools you have experience with, such as Apache Airflow or Databricks Jobs, emphasizing how you’ve used them to schedule and manage workflows effectively. Describe scenarios where you've improved data pipeline efficiency using these tools to demonstrate your expertise.

Effective collaboration involves understanding their data needs and providing them with well-structured datasets. Explain how you communicate regularly to gather feedback, adapt your data pipelines to analysis needs, and ensure the output aligns with their expectations. Providing an example of a successful collaboration can enhance your response.

Share specific challenges you've encountered, such as handling different data formats or managing large data volumes during ingestion. Detail the strategies you employed to resolve these issues, showing your problem-solving skills and creativity in overcoming obstacles in data processing.

Absolutely! Continuous Integration/Continuous Deployment (CI/CD) practices in data engineering are about automating the testing and deployment of data pipelines. Discuss how you’ve implemented CI/CD tools to streamline updates and ensure high-quality code and pipelines through automated testing and version control.

When discussing Delta Lake, emphasize its role in your data engineering processes. Explain how you’ve used it to provide ACID transactions, scalable metadata handling, and unifying batch and streaming data. Providing an example of how Delta Lake improved your data management or pipeline performance could be beneficial.

Mention key metrics such as data processing time, error rates, and data quality levels. Explain how you utilize monitoring tools to track these metrics and how you’ve used insights from monitoring data pipeline performance to make enhancements or optimizations.

Express your commitment to continuous learning through attending relevant conferences, participating in online courses, and being part of professional data engineering communities. Share any recent advancements or trends that you’ve found particularly interesting and how you plan to apply these insights at Softermii.