Data Engineer - OpenData Clinical

The primary responsibilities of a Data Engineer - OpenData Clinical at Veeva Systems include optimizing existing data tools, developing automated data validation frameworks, identifying data quality issues, collaborating across teams for solutions, and creating customized reports for stakeholders. You'll be tasked with ensuring the integrity and usability of the data within our OpenData Clinical project.

To apply for the Data Engineer - OpenData Clinical position at Veeva Systems, you should have a minimum of three years of experience in data engineering or analytics, with at least one year in a senior or leadership role. Proficiency in SQL, Python or R, and hands-on experience with data visualization tools like Tableau or Power BI are essential for the role. Strong English communication skills are also required.

Veeva Systems encourages continuous learning and professional growth for its Data Engineers. By providing access to training resources, mentorship programs, and opportunities to work on diverse projects, Veeva fosters a culture where employees can advance their careers, especially in leadership roles within data analytics and engineering.

Data Engineers at Veeva Systems can expect a flexible and inclusive work environment. As a company that supports remote working options and values employee input, you will thrive in an atmosphere that encourages collaboration and innovation, whether you choose to work from home or in our Lisbon office.

Absolutely! The role of Data Engineer - OpenData Clinical at Veeva involves extensive cross-functional collaboration. You will work closely with product management, data operations, and engineering teams, ensuring that all stakeholders receive high-quality data insights and solutions tailored to their needs.

In responses, you should emphasize specific examples where you've effectively managed and analyzed large datasets. Discuss the tools you used, the challenges faced, and how you tackled data quality issues while ensuring the outcomes aligned with project goals.

When asked this question, detail a framework you’ve created or implemented, outlining its structure, how it improved data quality, and its impact on decision-making processes. Highlight the technologies used and any cross-team collaborations involved.

Share specific strategies you utilize to prioritize tasks effectively, such as using project management tools, setting deadlines, and collaborating with teams to determine critical paths. Provide examples that illustrate your ability to meet tight timelines without compromising the quality of your work.

Discuss various data visualization tools you are proficient in, such as Tableau or Power BI. Cite examples of how you've used these tools to present data insights, emphasizing your ability to translate complex data into easily understandable visuals for stakeholders.

In your response, describe the process you typically follow to identify data quality issues, including methods like data profiling or anomaly detection. Provide an example of a specific issue you faced and explain how you resolved it, along with the outcome.

You should highlight your familiarity with cloud data tools and platforms, such as AWS or Google Cloud. Discuss any specific projects you've completed using these tools, detailing how they enhanced your tasks as a data engineer or analyst.

Emphasize the importance of communication in your data engineering role. Share how you ensure clear communication of data insights to non-technical stakeholders and how collaboration across teams assists in achieving shared goals.

Discuss the various avenues you pursue to stay informed, such as attending webinars, participating in online communities, and following thought leaders in the field. Mention any recent trends you’ve found particularly impactful and how they influence your work.

Provide insights into your experience with automating reporting systems, including tools and techniques you've successfully implemented. Highlight specific projects where your automation efforts resulted in efficiency gains and higher-quality reporting.

Share a detailed account of your experience working with a product management team. Describe the project, your contributions, and how your data analysis supported their objectives, showcasing your collaboration skills and impact on the project.