Data Engineer | MS Fabric - COM INGLÊS - Remoto

As a Data Engineer at Capco, your primary responsibilities include designing and managing data pipelines using Azure Data Factory, implementing storage solutions like Azure Data Lake Storage, and developing structures in SQL Server for business reporting. You'll also create interactive dashboards with Power BI and collaborate with various teams to support data-driven initiatives.

To be considered for the Data Engineer role at Capco, you should possess a Bachelor’s degree in Computer Science or a related field, along with experience in Azure Data Engineering and SQL Server. Familiarity with Power BI for creating dashboards and a strong understanding of ADLS is also essential. Additional certifications in Azure will be viewed favorably.

A successful Data Engineer at Capco should have expertise in Azure technologies, particularly Azure Data Factory and Azure Data Lake Storage. Proficiency in SQL is crucial, along with experience in developing business intelligence solutions using Power BI. Candidates should also exhibit problem-solving skills and a proactive attitude towards learning new technologies.

While experience with MS Fabric is considered a bonus, it is not a strict requirement for the Data Engineer position at Capco. What's essential is a solid background in Azure data engineering and familiarity with associated technologies. However, candidates who are eager to learn about MS Fabric are encouraged to apply.

Capco promotes an inclusive work culture where diversity is celebrated. As a Data Engineer, you will be part of a supportive environment that values collaboration, creativity, and innovation. The company actively encourages employees to participate in various internal committees focused on diversity and social responsibility, ensuring everyone's voice is heard.

When answering this question, provide specific examples of projects where you've used Azure Data Factory to build data pipelines. Highlight your ability to automate data workflows and integrate various data sources, and discuss any challenges you overcame during the process.

Explain techniques you use to enhance performance, such as optimizing data flows, utilizing partitioning in Azure Data Lake, and reducing latency. Discuss the importance of monitoring and iteratively refining processes based on performance metrics.

Discuss your approach to designing data storage solutions that balance costs with performance. Mention your experience in setting up Azure Data Lake Storage and how you decide between different storage options based on use cases.

Share details about your SQL proficiency level, specific SQL functionalities you're comfortable with, and instances where you wrote complex queries to extract or transform data. Provide quantitative results if possible to demonstrate your impact.

Choose a project that showcases your problem-solving skills. Clearly narrate the challenges you faced, the steps you took to address them, and the results achieved. Emphasize teamwork and collaboration where applicable.

Mention resources like online courses, webinars, or industry conferences that you regularly engage with. Highlight your proactive approach to learning and any communities or forums in which you participate to share knowledge with peers.

Discuss specific dashboards or reports you've created using Power BI, the features you utilized, and how you ensured the visualizations met stakeholders' expectations. Illustrate your understanding of data storytelling through visuals.

Explain the cloud services you've used, particularly those related to Azure. Discuss how cloud technologies help in scaling and deploying data solutions effectively and share hands-on experiences where possible.

Illustrate your experience working alongside other departments such as business, analytics, or IT. Highlight scenarios where collaboration led to successful project outcomes and how you communicated technical concepts to non-technical stakeholders.

Outline factors like accuracy, completeness, consistency, and timeliness. Discuss methods you've implemented or suggested in past roles to ensure high data quality across datasets, emphasizing its importance in business decisions.