Mid-Level Data Engineer

As a Mid-Level Data Engineer at Redhorse Corporation, your responsibilities include developing and maintaining data pipelines that facilitate efficient information flow. You will support data ingestion from various sources, monitor the health of the system, and automate configuration management. You'll also collaborate with functional groups to prototype innovative ideas and ensure that all processes adhere to security standards, especially within the DoD environment.

To qualify for the Mid-Level Data Engineer role at Redhorse Corporation, you should hold a Bachelor’s degree with 5-7 years of relevant experience or a Master’s degree with 3 years of experience. Experience with Big Data technologies, ETL processes, and Cloud environments like AWS or Azure is essential. Additionally, you must have an active Top Secret security clearance with SCI eligibility.

In the Mid-Level Data Engineer role at Redhorse Corporation, you'll work with a range of technologies including Apache Spark and Databricks for big data processing. You'll also automate ETL processes, utilize tools for configuration management, and engage with AWS, Microsoft Azure, or MilCloud 2.0, ensuring you stay updated with evolving technologies in the field.

The Mid-Level Data Engineer role at Redhorse Corporation directly contributes to national security initiatives by enabling data-driven decision-making. By developing and maintaining data pipelines, you'll ensure secure and efficient data flow, supporting the Department of Defense's mission and enhancing operational capabilities to respond to critical challenges.

At Redhorse Corporation, a Mid-Level Data Engineer works in a collaborative and innovative environment. You’ll engage with diverse functional teams and focus on continuous improvement and optimization of data processes. The role allows for independent initiative while being integral to high-impact projects that contribute to the greater mission of enhancing national security.

When discussing your ETL experience, highlight specific projects where you successfully designed or optimized ETL pipelines. Mention the tools you used, such as Apache Spark or Talend, and discuss the challenges you faced, how you overcame them, and the impact your work had on data integrity and accessibility.

To answer this question, illustrate your understanding of security measures related to data engineering. Talk about your familiarity with DoD Security Technical Implementation Guides (STIGs) and explain how you've implemented security best practices in past roles. Include examples of monitoring and auditing processes you've used to maintain a secure data environment.

Share a specific example of a technical challenge, detailing the context and complexity. Focus on your problem-solving process, the tools you employed, and the end results. This demonstrates your analytical thinking and ability to thrive under pressure, which are essential traits for a Mid-Level Data Engineer at Redhorse Corporation.

In your response, elaborate on the cloud services you've utilized and any roles where you designed or maintained cloud infrastructures. Include specific instances of using cloud components for data storage, processing, or automation and touch on the advantages these platforms provided for data engineering tasks.

Emphasize your commitment to continuous learning. Mention resources like online courses, industry conferences, or technical blogs that you follow. Sharing your insights on recent technologies you've implemented or explored shows that you are proactive in staying ahead in the data engineering field.

Discuss the role of data pipelines in ensuring efficient flow and processing of data from various sources. Highlight your experience in designing robust pipelines that facilitate data integration and streaming, ensuring timely access for analytics purposes and driving informed decision-making.

Mention the programming languages you’ve used frequently in your career, such as Python, Java, or SQL. Provide examples of how you have utilized these languages for data processing, automation, or building data models. This helps assess your technical proficiency relevant to the Mid-Level Data Engineer position.

Talk about your experiences working within teams that span different disciplines. Emphasize your communication skills, willingness to listen to others, and ability to understand diverse perspectives. Provide examples where collaboration led to significant project successes or innovative solutions.

Share specific monitoring tools or scripts you have used to track data pipeline performance and system health. Discuss the metrics you pay close attention to and how you handle alerts or failures to ensure system reliability and efficiency.

Illustrate your automation skills by discussing particular projects where you applied tools or scripts to automate repetitive tasks. Mention the impact that automation had on efficiency, error reduction, or speed of data processing, showcasing your forward-thinking approach as a data engineer.