Data Engineer

To qualify as a Data Engineer at Core One, applicants must hold a Bachelor’s degree in a relevant field from an accredited institution. Additionally, a minimum of 8 years of experience in data analysis is necessary, with some of that experience being recent, within the last 2 years.

As a Data Engineer at Core One, your primary responsibilities include designing, implementing, and maintaining data management systems that cater to intelligence needs. You will also work closely with data users to create optimal data architectures and ensure high data throughput and query performance.

Yes, the Data Engineer position at Core One requires an active TS/SCI clearance with a Polygraph. This is crucial due to the nature of the work and the level of access required to handle sensitive information.

Data Engineers at Core One will engage with various backend database technologies, including SQL and NoSQL systems. You'll also optimize data pipeline infrastructure to ensure efficient content handling and responsiveness for analysis.

Core One prides itself on being team-oriented and dynamic, with a strong commitment to exceptional employee performance and well-being. The organization not only provides competitive compensation but also fosters an environment that emphasizes personal and professional growth.

Highlight specific systems you've designed, discuss the challenges faced, and solutions implemented. Make sure to explain your decision-making process regarding the architecture and how it aligned with intelligence requirements.

Discuss specific projects where you've used SQL or NoSQL, the type of data managed, and your rationale for choosing one over the other. Show understanding of their strengths based on use cases.

Focus on techniques you've employed, such as indexing strategies, query refactoring, and database tuning. Be prepared with examples of measurable performance improvements.

Narrate a situation where you collaborated with stakeholders, illustrating how you gathered requirements, developed solutions, and ensured user satisfaction with the end result.

Talk about tools you have used, such as Apache NiFi or Talend. Explain why you find them effective for specific ETL tasks, giving examples of how they improved your workflow.

Address methods such as validation checks, error handling, and routine audits. Share instances where your attention to data quality made a significant difference in project outcomes.

Discuss a multi-faceted approach that can include database partitioning, sharding, or the use of distributed processing systems like Hadoop or Spark, including real-world applications of these strategies.

Share your understanding of relevant regulations such as GDPR or HIPAA and discuss best practices you incorporate to ensure compliance throughout the data lifecycle.

Mention specific tools like Tableau or Power BI that you've used for visualizing data. Offer examples that outline how these visuals aided in decision-making processes.

Express your thoughts on the importance of problem-solving skills in data engineering, along with examples of how you've applied this skill in real-world scenarios to overcome challenges.