Redshift / SQL Data Engineer

As a Redshift / SQL Data Engineer, you'll be tasked with crafting paginated reports, optimizing SQL queries, and ensuring data quality. You will also collaborate on technical solutions, oversee the deployment strategy, and actively work towards improving performance across data pipelines and dashboards.

Candidates for the Redshift / SQL Data Engineer role should ideally possess 5 to 8 years of SQL software development experience, along with 3 to 5 years in AWS, particularly with Redshift and S3. A strong background in data architecture and a keen understanding of data visualization tools is also highly beneficial.

Key skills for success in this position include strong SQL proficiency, effective collaboration and communication abilities, and expertise in data modeling and visualization tools. The ability to troubleshoot code, perform data quality checks, and deliver high-quality projects on time is crucial.

The work environment is flexible and supportive, emphasizing work-life balance and innovation. You will have the opportunity to participate in team building events and professional workshops, allowing for personal growth while working with a collaborative and motivated team.

Yes, there are ample opportunities for professional development, including participation in events, workshops, and hackathons. Additionally, you will receive a modern company laptop to assist in your work and help you stay up-to-date with the latest technologies.

When answering this question, highlight specific examples where you've improved query performance. Discuss the techniques you used and the impact those optimizations had on overall system performance.

Share your approach to data quality checks, including the processes you follow to identify and resolve discrepancies. Mention any tools or methodologies you have used to ensure the integrity of the data.

Discuss specific projects where you've utilized AWS and Redshift, detailing your role, the challenges you faced, and the results you achieved. This demonstrates your practical experience with the technology.

Highlight your communication skills and your approach to working within teams. Emphasize adaptability and openness to feedback while ensuring shared goals are met.

Provide a detailed scenario in which you identified an issue, the steps you took to troubleshoot it, and how you arrived at a solution. This showcases your problem-solving skills and technical expertise.

Explain your understanding of data architecture principles and how they guide your work. Describe specific instances where you've implemented or modified architectural frameworks in your previous roles.

Discuss your familiarity with Continuous Integration and Continuous Deployment, the tools you’ve used, and how these practices improved your development process or project outcomes.

Talk about the visualization tools you have experience with, such as PowerBI or D3, and explain why you prefer them, including factors like usability, integration with other platforms, or specific features that enhanced your work.

Share your method for managing multiple tasks, such as using task management tools or frameworks. Highlight your ability to stay organized while also being flexible to changes in project scope.

Express your enthusiasm for the constant evolution of technology in data engineering and share specific areas or tools you're eager to explore further or have already engaged with.