Data Engineer (ETL) - TS/SCI w/Poly

As a Data Engineer (ETL) at our company, your primary responsibilities include supporting data extraction, transformation, and load (ETL), mapping data flows, and addressing customer support challenges related to the ETL process. You'll also develop complex data flows and enhance existing pipelines, all while troubleshooting intricate issues and documenting your findings.

Candidates applying for the Data Engineer (ETL) position should hold a Bachelor’s Degree in Computer Science, Electrical or Computer Engineering, or a related technical discipline. However, equivalent combinations of education, technical training, or practical work experience may also be acceptable, particularly if you have 8+ years of relevant software engineering and ETL experience.

Essential skills for a Data Engineer (ETL) include proficiency in programming languages such as Python and SQL, familiarity with data tools like Elastic Search, and experience with Docker or Kubernetes. Additionally, strong problem-solving abilities and excellent organizational skills are key to managing the complexity of data operations efficiently.

A Data Engineer (ETL) will work in a fast-paced, dynamic environment that encourages multitasking and continual process improvement. You'll collaborate with a diverse team and engage in continuous learning within an innovative atmosphere that values your contributions and fosters your professional growth.

Our Data Engineers (ETL) enjoy a range of benefits, including 5 weeks of accrued vacation per year, fully covered health benefits, and a competitive 401k plan with a generous employer contribution. Additionally, we offer training reimbursement to support your continuous professional development and skills enhancement.

When discussing your ETL experience, focus on specific projects where you successfully designed, implemented, or optimized ETL processes. Mention the tools and technologies used, the challenges faced, and the outcomes achieved. Highlight your ability to troubleshoot issues and improve data flow efficiency.

Share your experience with various data transformation tools like Apache NiFi or Talend. Explain how you used these tools to extract, cleanse, and transform data, and provide examples of the impact your transformations had on data quality or project outcomes.

In your response, discuss a specific complex problem that required analytical thinking. Explain the steps you took to understand the issue, the techniques you employed to solve it, and how the resolution contributed to the success of your team or project.

Discuss your strategies for maintaining data quality during ETL processes, such as implementing validation rules, conducting regular data audits, and utilizing automated testing approaches. Emphasize the importance of establishing data governance and monitoring for continuous improvement.

Highlight your proficiency in relevant programming languages such as Python, SQL, or Java. Discuss how you have used these languages in previous projects to build data flows, create scripts for data transformation, or optimize performance.

Explain your hands-on experience with microservices architecture, particularly in how you’ve developed or maintained microservices in environments like Docker or Kubernetes. Provide examples of projects where microservices enhanced scalability or improved data processing performance.

Detail your approach to documentation, which may include methods for creating clear, concise, and comprehensive technical documentation. Mention tools you use for documentation, the importance of keeping documents up-to-date, and how it aids team collaboration.

Discuss the resources you leverage to stay informed about emerging technologies in data engineering, such as industry publications, professional networks, or online courses. Share an example of a new technology you've researched and its potential impact on your work.

Talk about any projects you have worked on using Apache NiFi. Explain how you designed data flows, the challenges you encountered, and how you optimized the flow for efficiency. Highlight any innovative solutions you implemented.

Provide examples of how you manage tight deadlines, such as using prioritization techniques, breaking down tasks, or maintaining open communication with your team. Discuss how you keep stress levels low while ensuring quality outputs.