Data Engineer/Architect

As a Data Engineer/Architect at Redhorse Corporation, you will be integral to the conceptualization and maintenance of data systems and pipelines. Your primary duties will include manipulating and managing data flows, supporting ETL processes, and translating data insights into strategic tools that aid operational decisions. You'll also be tasked with developing data mappings and models while ensuring that comprehensive documentation outlines both data and system functionalities.

To qualify for the Data Engineer/Architect role at Redhorse Corporation, candidates should possess a minimum of seven years of experience in data engineering or a related field. Proficiencies in programming languages such as Java, SQL, and Python, along with expertise in relational databases, data visualization tools, and active TS/SCI security clearance with Polygraph, are essential for this position.

A Data Engineer/Architect at Redhorse Corporation should be well-versed in various technologies including Java/J2EE, SQL, Python, and Shell Scripting. Familiarity with relational databases like Oracle and MySQL, GPU processing, data visualization tools such as ElasticSearch, and frameworks for machine learning and data science, such as Keras or TensorFlow, will be beneficial for success in this role.

In the role of Data Engineer/Architect at Redhorse Corporation, your work contributes directly to national security by designing and maintaining data systems that enable government agencies to make informed decisions based on accurate data insights. By solving complex data challenges and implementing innovative technologies, you support critical missions that address vital national issues.

The work environment for a Data Engineer/Architect at Redhorse Corporation is fast-paced and dynamic, promoting continuous process improvement. You'll collaborate with a talented and motivated team, where creativity and innovation are valued. This setting encourages you to explore cutting-edge technologies while managing significant data projects that make a real-world impact.

When discussing your experience with ETL processes, highlight specific projects where you've successfully implemented data extraction, transformation, and loading. Emphasize the tools you used, any challenges faced during these processes, and how you optimized them for efficiency.

Detail your proficiency in programming languages relevant to the Data Engineer/Architect role, such as Java, SQL, and Python. Describe scenarios where you utilized these languages for database management, data manipulation, or implementing machine learning algorithms, showcasing your ability to select the appropriate language for the task.

Discuss your strategies for managing large-scale data sets, such as using partitioning for databases, indexing for faster query performance, or leveraging GPU processing. Provide examples of how you've addressed scalability and efficiency in previous projects.

Talk about your experience using data visualization tools like ElasticSearch or OmniSci, explaining their importance in making data insights more accessible. Share examples of how you've transformed complex data sets into intuitive visual formats to aid decision-making.

Highlight specific machine learning methodologies you have applied, detailing the frameworks used, such as Keras or TensorFlow. Provide an example of a project where machine learning was implemented to provide predictive analytics or solve a significant challenge.

Explain your systematic approach to database maintenance, including regular performance checks, data quality assessments, and implementing backup solutions. Discuss how you ensure data integrity and usability, along with examples of how you resolved past issues.

Discuss your commitment to professional development by highlighting your methods for staying updated with industry trends and technologies. Mention any relevant courses, certifications, or communities you engage with to enhance your skills continuously.

Prepare a compelling narrative about a complex project where you played a key role, detailing your contributions, the technologies utilized, and the project's eventual impact. Focus on how your work facilitated improvements or supported critical decisions.

Share your preferences for version control tools such as GitHub or GitLab. Explain why you favor these tools, perhaps mentioning their collaboration features, ease of use, or how they aid in maintaining code integrity across projects.

Discuss your organization and prioritization strategies that enable you to thrive in a fast-paced setting. Share specific techniques you've used to manage multiple projects or deadlines, emphasizing your adaptability and focus on results.