Senior Data Engineer

As a Senior Data Engineer at 3Pillar Global, your primary responsibilities include collaborating with scrum teams to deliver product stories based on customer priorities, engaging with stakeholders for input and knowledge sharing, and developing big data pipelines utilizing Microsoft BI technologies. You'll be integral in ensuring data integration and flow, which is essential for effective reporting and analytics.

Candidates applying for the Senior Data Engineer position at 3Pillar Global should have experience with Amazon Web Services (AWS) tools like DMS, Glue, and Step Functions as well as a strong foundation in Python and Typescript. Familiarity with data orchestration, relational databases, and experience in designing data environments for analytics are also critical to success in this role.

At 3Pillar Global, we truly value the professional development of our team members. As a Senior Data Engineer, you will have access to career growth opportunities, such as skill development programs and mentoring. We encourage continuous improvement and ensure that all employees can grow their expertise in technology and data engineering through various learning initiatives.

For the Senior Data Engineer position at 3Pillar Global, strong skills in Python and Typescript are essential. Additionally, proficiency in SQL, particularly T-SQL, is necessary for writing, optimizing, and troubleshooting queries. Familiarity with automation technologies such as Linux shell scripting is also beneficial for managing data workloads.

The workplace culture for Senior Data Engineers at 3Pillar Global is one of collaboration and open communication. Our core values guide every aspect of our work, leading to a supportive environment where team members feel encouraged to innovate and contribute. With a focus on professional wellbeing and work-life balance, you can thrive here.

In answering this question, discuss specific AWS services you have used, such as DMS, Glue, and Step Functions. Highlight any projects where you designed, implemented, or optimized data pipelines using these technologies, emphasizing your contributions and the outcomes achieved.

To answer effectively, outline your approach to data quality management, including data validation techniques, monitoring tools, and your experience with data cleansing methods. Provide examples of how you identified and addressed data quality issues in previous projects.

For this question, share a specific project that presented challenges, focusing on the problems you encountered, your analytical approach to solving them, and the successful results. This highlights your problem-solving abilities and technical expertise.

Discuss any orchestration tools you have used, such as ActiveBatch, and share how you implement job scheduling and automation to streamline workflows. Provide examples where your orchestration decisions enhanced efficiency or reliability in the data pipeline.

Share your method for engaging with stakeholders, such as regular communication and presenting clear updates on project statuses. Highlight the importance of understanding their goals and how you adapt your work to meet their needs effectively.

Detail your experience with Microsoft Power BI, including how you design datasets, create reports, and develop dashboards. Share specific examples that illustrate how your visualizations have provided actionable insights to stakeholders.

When answering, describe your systematic approach to identifying the root cause of data issues, such as analyzing logs, utilizing specific debugging tools, or following data lineage. Emphasize how your critical thinking skills play a role in resolving these challenges.

Highlight best practices you adhere to when writing SQL, such as optimization techniques, maintaining readability, and ensuring code reuse through modular functions. Share examples of how you applied these practices in previous roles.

Discuss methods you utilize to keep your skills current, such as attending webinars, reviewing industry publications, and participating in online data engineering communities. This demonstrates your commitment to continuous learning and improvement.

Describe your familiarity with different data modeling techniques and how you have applied them to design efficient databases in various projects. Be specific about the outcomes that resulted from effective modeling in terms of performance and usability.