Data Platform Engineer - (Remote - US)

As a Data Platform Engineer at Jobgether, your primary responsibilities include designing and building scalable data pipelines, optimizing ETL frameworks, collaborating with cross-functional teams, and ensuring data quality and security. You'll play an integral role in improving system observability and implementing robust monitoring practices.

To qualify for the Data Platform Engineer position at Jobgether, you should have a minimum of four years of experience in software development, particularly as a data or backend engineer. Proficiency in Python and SQL is required, along with familiarity with technologies such as Airflow, Docker, and AWS.

Jobgether offers a flexible remote work environment for the Data Platform Engineer role, allowing you to work from anywhere in the US. The company promotes a collaborative culture, ensuring that even remote team members can engage and contribute effectively.

The Data Platform Engineer position at Jobgether comes with several enticing benefits including unlimited vacation, technology stipends, full medical coverage, 401(k) with immediate company match, and generous parental leave provisions, among others.

The application process for the Data Platform Engineer role at Jobgether includes an AI-powered 3-step screening that evaluates your qualifications and CV. Top candidates will be notified within 7 days about their progression in the recruitment process.

When answering this question, highlight specific projects where you designed and built data pipelines. Discuss the technologies you used, the challenges you faced, and how you overcame them. Mention any improvements in data throughput or reliability as a result of your efforts.

In response, emphasize strategies you use to maintain data quality, such as implementing rigorous validation checks, using monitoring tools, and engaging in continuous testing. Provide examples of how your actions prevented data discrepancies in past projects.

List tools like Apache Airflow or custom-built frameworks you're familiar with. Describe how you have used them for automation and orchestration of data workflows, and the impact they had on improving efficiencies.

Provide a specific example of a technical challenge, detailing what steps you took to diagnose the problem and how you arrived at a solution. Showcase your problem-solving skills and your ability to work under pressure.

Speak to your experience in collaborating with product managers, data analysts, and other stakeholders. Discuss your approach to understanding their data needs and how you ensure effective communication and workflow integration.

Focus on your experience utilizing AWS services such as S3, EC2, or Redshift in your data platform projects. Explain how you've leveraged cloud technologies for scalability and flexibility in your solutions.

Mention specific resources such as blogs, podcasts, or communities you engage with. Highlight any recent trends or technologies you have started applying in your work.

Discuss tools you have used for monitoring data workflows and how you implement observability practices. Provide examples of how you've used monitoring to proactively address issues before they affect users.

Detail the version control systems you’ve used, ideally Git, and how you manage branching and merging. Describe your involvement in CI/CD processes, emphasizing how this contributes to your workflow efficiency.

Reflect on your passion for data and analytics, and how it drives your work. Share personal stories or insights into projects that excited you, illustrating your enthusiasm for finding data-driven solutions.