(847) Semi Senior Data Engineer

As a Semi Senior Data Engineer at Nearsure, you will design and maintain interactive dashboards using Power BI, collaborate with data analysts to turn business needs into data insights, and develop ETL processes using tools like Microsoft Fabric and Azure Data Factory. Your role is pivotal in optimizing data solutions that empower insightful decisions, connecting various stakeholders across projects.

To become a Semi Senior Data Engineer at Nearsure, candidates should have a Bachelor’s Degree in Computer Science or a related field, along with at least 3 years of experience working with Power BI and SQL Server. Familiarity with data warehouse environments and star schema architectures is crucial, while advanced English proficiency is essential for effective communication with US clients.

At Nearsure, the work culture for a Semi Senior Data Engineer is supportive and collaborative, promoting autonomy and open communication. Team members participate in fun activities like coffee breaks and tech talks, creating camaraderie and allowing everyone to enjoy their remote work experience while building lasting relationships.

As a Semi Senior Data Engineer at Nearsure, you can expect a competitive salary, 100% remote work with access to coworking spaces in LATAM, paid time off according to local laws, sick leave, and a special day off during your birthday week. Additionally, an annual refundable credit for perks and opportunities to engage in team-building activities await you!

Nearsure prioritizes work-life balance for its Semi Senior Data Engineers through a dedicated People Care team that supports your needs from day one, offering help with time off requests and wellbeing initiatives. Plus, accessible coworking spaces and flexible remote work options allow you to enjoy personal time while excelling professionally.

When answering this question, share specific projects where you utilized Power BI to create dashboards or reports. Discuss the tools and techniques you applied, any challenges faced, and the overall impact your contributions had on data-driven decision-making within the organization.

For this question, outline the ETL tools you have used, such as Azure Data Factory or Microsoft Fabric. Discuss how you approach data extraction, transformation, and loading to ensure efficient data flow, including any experience you have with scheduling and monitoring these processes.

Illustrate your approach to stakeholder collaboration by mentioning your communication style, strategies for requirement gathering, and the importance of clarifying expectations. Emphasize your ability to translate technical concepts into terms non-technical stakeholders can understand.

Here, provide a concrete example of a data project you worked on, detailing your specific role, the improvements made, and the final outcomes. Highlight technologies used, problems solved, and the overall impact on business processes or data accessibility.

Discuss data model design principles, indexing strategies, and performance optimization techniques you’ve employed. Highlight your understanding of star schema architectures and how they relate to efficient query performance in a data warehouse.

Detail the measures you take to ensure data quality, such as validation checks, consistency rules, and monitoring procedures. Provide examples of how you’ve identified and resolved data quality issues in past roles for a broader context.

Explain that effective communication is crucial for understanding requirements, sharing insights, and collaborating across functional teams. Highlight examples of how strong communication skills have facilitated project success in your previous experiences.

Mention specific resources you use to stay informed about the latest trends, tools, and best practices in data engineering. This could include online courses, forums, blogs, or industry conferences that you engage with regularly.

When answering, highlight your familiarity with SQL queries, stored procedures, performance tuning, and database administration tasks. Detail specific projects where you utilized SQL Server effectively to manage data and derive insights.

Share your passion for data engineering and discuss the aspects you find most rewarding, whether that be solving complex data problems, creating impactful visualizations, or collaborating with diverse teams to achieve common goals.