(Senior) Data Engineer (m/f/x) onsite or remote in Germany

As a (Senior) Data Engineer at Scalable Capital, your responsibilities include developing scalable cloud-based data solutions, designing AWS-based streaming and batch data processing systems, and building a financial data warehouse that complies with regulatory standards. You'll be preparing and cleaning both structured and unstructured data, crafting high-quality data models for advanced analytics, and collaborating closely with data scientists and product teams to enhance our offerings.

To qualify for the (Senior) Data Engineer position at Scalable Capital, you should possess an excellent university degree in computer science, mathematics, or related fields, along with relevant working experience. Familiarity with designing and operating data pipelines in AWS, advanced SQL skills, solid programming skills in Python, and experience with AWS services are essential. Knowledge of Java, Kotlin, and infrastructure-as-code tools will give you an edge!

Working as a (Senior) Data Engineer at Scalable Capital offers outstanding career growth opportunities. Being part of one of Europe's fastest-growing Fintech startups, you will gain invaluable experience in cutting-edge data technologies and financial services. You’ll also have access to in-house training sessions and an individual education budget, allowing you to continuously upskill and stay ahead in your career.

At Scalable Capital, the work environment is dynamic, inclusive, and engaging. You'll be part of an international team of talented individuals who are passionate about creating the best products for our clients. Enjoy a modern office setup in Berlin or the flexibility of remote work within Germany, along with collaboration and knowledge-sharing sessions that foster growth and teamwork.

As a (Senior) Data Engineer at Scalable Capital, you will work with a variety of technologies, including AWS services such as S3, Redshift, Athena, and Glue. You'll also utilize Python, SQL (including advanced concepts), along with infrastructure-as-code tools like Terraform. Your knowledge of data modeling, machine learning, and AI will be integral to driving our innovative solutions.

Discuss specific AWS services you've worked with, such as S3, Redshift, or Glue, and detail any projects where you designed or managed data pipelines in AWS. Highlight your experience with batch and streaming data processing to illustrate your understanding.

Explain your systematic approach to handling both structured and unstructured data, emphasizing techniques you use to clean, validate, and prepare data for analysis. Mention any specific tools or programming languages that facilitate this process.

Share your methodology for constructing data models, including your considerations for scalability, performance, and data integrity. Discuss any frameworks you've used and how you apply best practices in architecture and documentation.

Describe your experience working cross-functionally, emphasizing communication and feedback loops. Provide examples of successful collaborations where your input as a Data Engineer significantly contributed to a product outcome.

Select a specific project where you faced obstacles, detailing the challenge, your strategy for overcoming it, and the outcome. This highlights your problem-solving skills and perseverance.

Discuss the tools and technologies you've used for dashboard creation, like Tableau, Power BI, or custom solutions, and explain how these helped stakeholders make data-driven decisions.

Talk about your commitment to writing well-architected, testable, and documented code. Share any best practices you maintain for ensuring that the documentation is up-to-date and useful for future developers.

Explain how Data Engineers provide the foundational data structures and pipelines that enable machine learning projects. Highlight your understanding of the interplay between data engineering and machine learning processes.

Share your strategies for continuous learning, such as following industry blogs, attending webinars and conferences, or participating in online courses. This shows that you're proactive about your professional development.

Describe your familiarity and experience working in agile environments, mentioning specific frameworks you’ve utilized, such as Scrum or Kanban. Discuss how this approach has positively impacted your project delivery.