Senior Data Engineer

As a Senior Data Engineer at Oowlish, you will design, develop, and manage data pipelines using Apache Airflow. You will write efficient Python code for scalable ETL/ELT processes and own the evolution of our internal data infrastructure and integration pipelines. Monitoring data quality, reliability, and performance will also be key aspects of your role, along with collaborating with international teammates in English.

To qualify for the Senior Data Engineer role at Oowlish, candidates should have at least 5 years of experience in data engineering or similar technical roles. Proficiency in Python and hands-on knowledge of Apache Airflow are essential. Familiarity with PostgreSQL, AWS, or Google Cloud could be an advantage, but having a background in startups and being comfortable working autonomously will set you apart.

Absolutely! Oowlish offers the convenience of remote work for the Senior Data Engineer position. This enables you to maintain a work-life balance that suits your personal and professional needs while working collaboratively with a diverse, global team.

Oowlish fosters a welcoming and collaborative team culture. As a Senior Data Engineer, you'll work with a high-impact team in a startup environment that values autonomy and encourages knowledge sharing. You can expect to engage with a team that is committed to innovation and collaborative success across various projects.

Oowlish provides a comprehensive benefits package for Senior Data Engineers, including flexible work hours, competitive compensation, substantial opportunities for career growth, and various perks like internet allowances, anniversary bonuses, and wellness programs. You will also be part of international projects, enhancing your career globally.

When answering this question, highlight specific scenarios where you've successfully implemented Apache Airflow in your previous roles. Discuss how you've used it to create and manage workflows, ensuring data pipeline reliability. Providing metrics or outcomes from your work can impress the interviewers.

Discuss your approach to monitoring data quality, such as implementing validation checks at various stages of the data pipeline. Mention any tools you use to track data quality metrics and your experience with establishing best practices that help maintain high data reliability.

Approach this question methodically: identify common performance bottlenecks, demonstrate your problem-solving process, and possibly share examples of past experiences where you successfully resolved performance issues in data pipelines.

Here, emphasize your hands-on experience with both ETL and ELT processes. Explain how you've written clean, scalable Python code for data extraction, transformation, and loading. Mention any frameworks or libraries you've utilized and the impact your work has had.

Discuss how you assess the urgency and impact of tasks. Share techniques you use for prioritization, such as Agile methodologies or project management tools, and highlight your flexibility in adapting to changing project needs while ensuring key deliverables are met.

Highlight your communication strategies, such as regular check-ins or the tools you use for collaboration like Slack or Zoom. Talk about your experience in fostering a team spirit and making effective contributions even while working remotely.

Prepare a detailed description of a specific project where you were primarily responsible. Focus on your role, the challenges you faced, the solutions you implemented, and the overall results, showcasing your technical expertise and problem-solving abilities.

Share your direct experience with cloud platforms, discussing specific services you’ve utilized and how they fit into your data engineering projects. Explain any instances where cloud services contributed to enhancing data pipelines or data processing.

Discuss the key factors you keep in mind, such as scalability, data integrity, and processing efficiency. Sharing examples of how you've successfully designed pipelines in the past will give your answer depth.

Explain your learning process. Mention your resources, such as tutorials, documentation, or courses, and provide examples of technologies you've successfully learned and implemented in previous roles, displaying your commitment to continuous professional development.