(830) Senior Data Engineer

To excel as a Senior Data Engineer at Nearsure, you should possess a Bachelor's Degree in Computer Science or a related field, complemented by over five years of experience in Data Engineering. Proficient knowledge of Python programming and SQL is essential, along with familiarity with Cloud technologies, Microservices, and CI/CD tools. Additionally, a strong command of English is required for effective communication with US clients.

Nearsure prioritizes the well-being of its remote Senior Data Engineers through a dedicated People Care team. From the moment you join, you have access to support for anything, whether it’s taking time off, engaging in team-building activities, or simply needing someone to connect with. We foster an environment where autonomy and collaboration go hand in hand.

The onboarding process for a Senior Data Engineer at Nearsure is designed to make your transition smooth and welcoming. After an initial chat with our recruiters discussing your background, you'll participate in a technical interview where our developers will assess your skills. If you progress, you will have the opportunity to meet our client, further strengthening your potential to join our exceptional team.

As a Senior Data Engineer at Nearsure, you'll enjoy a host of benefits such as a competitive USD salary, paid time off based on your national laws, national holidays, a refundable annual credit for perks, and a special day off during your birthday week. These are designed to ensure you have ample time for rest and personal engagements while fostering a supportive team environment.

Nearsure champions team building among remote employees by organizing engaging activities like coffee breaks, tech talks, and after-work gatherings, providing a chance to foster relationships and community spirit. These interactions ensure that even while working from diverse locations, you feel connected and valued as part of the Nearsure family.

The work culture at Nearsure is vibrant, inclusive, and built on the principles of mutually respectful communication, autonomy, and diversity. As a Senior Data Engineer, you'll enjoy a supportive network that values your contributions and encourages open dialogue, allowing you to express ideas and collaborate effectively with your peers.

As a Senior Data Engineer at Nearsure, you will engage in a variety of projects aimed at developing robust data-driven solutions for our clients. Your responsibilities will include writing validation codes for SQL, testing backend APIs, and creating tools to enhance the productivity of the development team, all crucial to ensuring seamless operation in data environments.

When discussing your experience with SQL during the interview, focus on specific projects where you utilized SQL extensively. Highlight the complexity of queries you have generated, any performance improvements you implemented, and the overall impact those SQL strategies had on the project's success. Provide examples of how you ensured data integrity and optimized queries for speed.

In your response, detail your systematic approach to testing and validation. Discuss how you write and execute test cases that check the business logic encoded within the SQL databases. Mention any specific methodologies you've used, such as boundary value analysis or equivalence partitioning, which help ensure comprehensive coverage and data accuracy during validations.

Candidates should highlight their experience with CI/CD tools such as Jenkins, Travis CI, or GitLab CI. Discuss how you've implemented automated testing and deployment processes that have improved project workflow. Sharing concrete examples of how these tools streamlined your projects will showcase your ability to manage robust data software development effectively.

When addressing remote collaboration, emphasize the tools and techniques you employ to ensure effective communication with your team. Mention platforms like Slack, Zoom, or Jira that facilitate collaboration. Discuss how you maintain engagement with your colleagues and any strategies you use to ensure everyone is on the same page despite geographical distances.

In your answer, describe your systematic approach to managing backlogs. Discuss prioritization techniques such as MoSCoW or the Eisenhower matrix, and how you assess the impact and urgency of backlog items. Mention any experiences where you successfully reduced backlog issues and met project deadlines efficiently.

Reflect on a specific data project that presented challenges, such as integration issues or data quality problems. Describe the steps you took to analyze the obstacles, the collaborative efforts with your team, and the resolutions you implemented. Highlight the results achieved and what you learned from the experience, showcasing your problem-solving skills.

Your answer should encompass the different cloud platforms you have used, such as AWS, Azure, or Google Cloud. Discuss specific services related to data engineering, like data storage solutions (e.g., S3, Blob Storage), data processing (e.g., AWS Lambda, Dataflow), and any relevant experience in deploying or managing databases in these environments.

In your response, share the resources and communities you engage with to stay informed about emerging trends. Mention online courses, webinars, or conferences you participate in, and highlight any professional networks or forums where you actively discuss and learn about advancements in the field. This shows your commitment to continuous learning.

Take this opportunity to express your passion for data engineering. Discuss the satisfaction of turning raw data into actionable insights, the impact of your work on business decisions, and how you enjoy solving complex problems through innovative solutions. Personal stories about your contributions to successful projects can make your response more engaging.

Describe your ability to work effectively across different teams, emphasizing communication, empathy, and understanding the distinct needs of other areas like product management or software development. Discuss instances where you've fostered collaboration to ensure that data engineering needs support overall project goals, showcasing your interpersonal skills and teamwork.