Senior Data Engineer (Python)

As a Senior Data Engineer (Python) at Capco Poland, your primary responsibilities will include designing, developing, and maintaining robust data pipelines tailored for both batch and streaming data processing. You will work closely with cross-functional teams to align data requirements with business needs, optimize workflows for performance, and troubleshoot any pipeline issues to ensure minimal downtime. Your role is pivotal in ensuring data quality and integrity across the entire data lifecycle.

To be considered for the Senior Data Engineer (Python) role at Capco Poland, you should have a minimum of 5 years of relevant experience in a data engineering or big data engineering capacity. A university degree in computer science, mathematics, or a related field is required, along with strong SQL skills and proficient programming abilities in Python. Experience with Spark and Hadoop, as well as an understanding of OOP and agile frameworks like Scrum, will set you apart.

For the Senior Data Engineer (Python) position at Capco Poland, you will need a solid foundation in programming with Python, experience in database technologies using SQL, and familiarity with big data tools such as Spark and Hadoop. Additionally, knowledge of cloud platforms like GCP and tools like Kafka or Apache NiFi is highly beneficial. Being adept in troubleshooting and maintaining data pipelines while focusing on data security, performance, and scalability is crucial.

As part of the Capco Poland team, employees enjoy a range of benefits including flexible working conditions, participation in various employee benefits programs, access to 3,000+ business courses on platforms like Udemy, and support for ongoing learning opportunities. You will also be part of a vibrant culture that cares about personal development, health insurance options, and a referral program, making it a fantastic environment to grow your career.

The application process for the Senior Data Engineer (Python) role at Capco Poland involves several steps, starting with a screening call with the recruiter, followed by a technical interview as the first stage. After successfully navigating that, you will have a client interview to discuss your fit for the role. Finally, you can expect feedback and an offer if selected. This structured approach ensures that candidates are aligned with our culture and requirements for success.

When answering, focus on a specific example where you faced a significant challenge in data pipeline development. Detail the problem, the technologies you used (like Python or Spark), the steps you took to troubleshoot, and ultimately how you achieved a resolution. Highlight any positive outcomes that resulted from your solution.

In your response, discuss your approach to data validation and cleaning processes. Mention specific practices such as implementing unit tests for data pipelines, regular audits, and the use of tools that assist in monitoring data quality metrics while ensuring adherence to compliance and security standards.

Share your commitment to continuous learning, such as following industry blogs, participating in webinars, or engaging in online courses. Mention any relevant conferences or community events you attend to network with peers and share knowledge in the field of data engineering.

Provide a thoughtful approach that includes analyzing job execution plans, identifying bottlenecks, and optimizing Spark configurations, such as partitioning strategies and caching techniques. Discuss your experience with performance tuning to showcase your practical knowledge.

Emphasize the importance of communication and teamwork in successful data projects. Share an experience where you worked with teams outside of data engineering, detailing how you ensured requirements were met and how you fostered collaboration to achieve project goals.

Discuss your preferred toolset, mentioning Python, Spark, SQL, and any cloud platforms you've worked with, such as GCP or Azure. Explain why you prefer these technologies, citing their strengths in scalability, reliability, and performance for data pipeline creation.

Provide a clear definition of both concepts, explaining how batch processing handles large volumes of data in bulk at scheduled intervals, while streaming processes real-time data continuously. Illustrate your understanding with practical examples from your experience.

Address your awareness of data privacy regulations and security best practices. Discuss your strategies for protecting sensitive data, implementing access controls, and ensuring compliance with relevant laws like GDPR while designing data solutions.

Discuss your familiarity with agile frameworks like Scrum. Provide examples of how you’ve applied agile practices in your projects, such as participating in sprint planning, daily stand-ups, and retrospectives, to emphasize your adaptability and collaborative spirit.

Share your passion for the financial industry, highlighting your interest in its fast pace and the importance of data-driven decision-making. Reflect on how your role as a Data Engineer can significantly impact business processes and client success in a sector that is continually evolving.