Senior Data Engineer (AS)

As a Senior Data Engineer at Blue Coding, your primary responsibilities will encompass leveraging various data sources to support stakeholders in product development. This includes collaborating with business stakeholders to identify their goals, creating solutions that standardize data approaches, and ensuring the reliability and documentation of data. You'll also play a vital role in improving data availability, setting up a reporting system in BigQuery, and revamping data orchestration to enhance delivery times.

To qualify for the Senior Data Engineer role at Blue Coding, candidates should possess strong hands-on experience with Python and its data processing tools. Additional qualifications include familiarity with cloud environments (Docker, Kubernetes, GCP, or Azure), experience with Spark or PySpark, and expertise in designing scalable data pipelines. Advanced SQL capabilities and the ability to communicate technical solutions to a diverse audience are essential. Experience with Data Warehouses, data lineage, and infrastructure-as-code tools like Terraform will also strengthen your application.

Absolutely! The Senior Data Engineer position at Blue Coding is fully remote, allowing you to work from anywhere in Latin America. We embrace a flexible schedule to accommodate working hours within U.S. time zones, which enables our team members to maintain a healthy work-life balance.

At Blue Coding, we believe in fostering a culture of continuous learning and growth. As a Senior Data Engineer, you'll be encouraged to learn new technologies and tackle complex problems. We promote collaboration, allowing team members to share insights and best practices, while also offering opportunities to engage in various initiatives that contribute to both personal and professional development.

The culture at Blue Coding is vibrant, friendly, and inclusive. We pride ourselves on maintaining a culturally diverse workforce where team members are united by collaboration, respect, and shared goals. Regular team-building activities, such as movie nights and trivia competitions, help foster connections among coworkers, creating a supportive atmosphere where everyone is encouraged to thrive.

When answering this question, emphasize your hands-on experience with Python and specifically mention the projects where you've utilized pandas and NumPy. Discuss how you've leveraged these tools for data manipulation and analysis, highlighting specific challenges you overcame and the impact of your work on prior projects.

In your response, focus on the processes you implement to maintain data quality, such as rigorous testing, documentation, and data validation techniques. Mention any specific tools or frameworks you’ve employed to track data lineage and ensure consistent data quality throughout your projects.

Be sure to specify the cloud platforms you've worked with, such as GCP or Azure, and describe any significant projects you've completed. Highlight your experience with services like Kubernetes and Docker, and detail how your work directly benefited the project or team objectives.

Here, you should outline the steps you've taken to set up a reporting system in BigQuery, including data replication and infrastructure configuration. Discuss how you utilized dbt for model creation and how the final reporting endpoints provided valuable insights to stakeholders.

You can emphasize your familiarity with Agile practices, detailing how you've participated in sprints, daily stand-ups, and retrospectives. Discuss how collaborating in Agile environments enhances your adaptability and communication skills, ultimately leading to successful project outcomes.

In your answer, focus on the techniques you use to simplify complex data topics, such as using analogies, visual aids, or breaking information into digestible parts. Highlight your experience in presenting data findings and reports to help stakeholders make informed decisions.

Discuss your methodology for building scalable data pipelines, mentioning factors like modular design, using ETL tools, and ensuring the pipelines can handle growing datasets. Highlight the importance of futures-proofing your designs for long-term success.

When addressing this, share specific examples of observability techniques you've used, such as logging, monitoring, or implementing automated tests. Discuss how these practices have led to improved data accuracy and reduced downtimes in your projects.

Detail your involvement with various databases, emphasizing the projects in which you’ve utilized both relational (e.g., MySQL, PostgreSQL) and non-relational databases (e.g., MongoDB, Cassandra). Discuss the scenarios where you've chosen one type over the other and why it was advantageous.

Convey your commitment to professional development by discussing the resources you use, such as online courses, webinars, industry blogs, and attending conferences. Highlight any communities or networks you're part of that keep you informed about trends and technologies in data engineering.