Junior Data Engineer, Fintech

As a Junior Data Engineer at Optasia, you will be responsible for collaborating with various teams to design and support end-to-end data processing workflows. You'll develop and optimize core libraries for batch data ingestion, as well as design new libraries for real-time data streaming. In essence, you will play a pivotal role in enhancing Optasia's big data infrastructure and ensuring that our data processing pipelines are efficient and scalable.

To be considered for the Junior Data Engineer role at Optasia, you should have a Bachelor's or Master's degree in Computer Science or Informatics. Additionally, we are looking for candidates with working experience in programming languages such as Scala or Python, and familiarity with Apache Spark. Experience with both relational and NoSQL technologies is also important, along with knowledge of developing data processing pipelines.

In your role as a Junior Data Engineer at Optasia, you will have the opportunity to work with a range of modern technologies. Key tools include Apache Spark for data processing, both relational databases and NoSQL technologies. Familiarity with Apache Hadoop ecosystem components such as YARN, HDFS, and MapReduce will be advantageous. We also value experience with data flow engines like Apache Airflow for managing complex workflows.

While some work experience is beneficial for the Junior Data Engineer role at Optasia, we are open to candidates with varying levels of experience. If you have coding experience with programming languages like Scala or Python and knowledge of big data technologies like Apache Spark, we want to hear from you! Your eagerness to learn and collaborate will be equally valued.

Optasia offers a comprehensive benefits package to its Junior Data Engineers, including flexible remote working options, a competitive remuneration package, and extra days off, including a special day off for your birthday! You'll also enjoy performance-based bonuses, comprehensive private healthcare, access to continuing education opportunities, and an engaging work environment with various wellness activities.

When answering this question, focus on specific projects where you've developed or managed data processing pipelines. Discuss the technologies you used, such as batch processing or real-time streaming. Highlight any challenges you faced and how you overcame them, emphasizing your problem-solving skills and teamwork.

Be prepared to discuss your experience with programming languages like Scala or Python. Provide examples of projects where you've utilized these languages for data processing tasks. If you have learned or are learning other relevant languages, mention them, too, as it shows your commitment to growth.

Discuss any hands-on experience you have with Apache Spark, including the types of projects you've worked on. You could mention specific functionalities of Spark that you've used, like Spark SQL or MLlib, and how they contributed to the success of the projects.

This is a great chance to showcase your knowledge of database technologies. Explain how relational databases use structured schemas while NoSQL databases provide flexibility with unstructured data. Provide examples of when you would use one over the other, perhaps referencing projects where you've used both types.

Reflect on your understanding of secure coding practices and data protection regulations. You can mention specific guidelines, like OWASP or NIST, and discuss how you have implemented secure coding practices in previous projects or how you would prioritize data security in your role at Optasia.

Here, share experiences from previous jobs or projects where teamwork was essential. Illustrate your ability to communicate effectively and resolve conflicts by focusing on collaboration and understanding different perspectives.

Use the STAR method to structure your response. Share a specific challenge related to data engineering, detailing the Task, Action, and Result. Emphasize your analytical skills and your proactive approach to overcoming obstacles.

Talk about how you engage in continuous learning, whether through online courses, webinars, or reading articles/blogs. Mention any specific resources you turn to for staying current in the field of data engineering, as it emphasizes your commitment to professional growth.

Provide a clear definition of ETL (Extract, Transform, Load) and describe each component of the process. Share examples of how you've been involved in ETL processes, highlighting the tools and methodologies you've used.

When responding, reflect on what excites you about Optasia's mission and values. Discuss how they align with your career goals and how you see yourself contributing to the company's success, emphasizing your enthusiasm for innovation and learning.