Senior Data Architect

As a Senior Data Architect, your primary responsibilities will include designing and maintaining robust data models to support complex analytic and operational requirements. You will engineer scalable data solutions using ECL, SQL, Python/Spark, or Java, and analyze structured datasets to optimize usability. Collaborating closely with mission stakeholders, you'll translate their data needs into technical solutions and develop strategies for ensuring data accessibility and governance.

To be considered for the Senior Data Architect role, you should have at least 5 years of experience in data modeling or data architecture. A Bachelor of Science degree in Computer Science, Engineering, or a related field is required, along with extensive programming experience in ECL, SQL, Python/Spark, or Java. Excellent communication skills and an ability to work with structured data are also crucial.

Essential skills for a Senior Data Architect at our company include proficiency in data modeling and structured data manipulation. You will need strong programming abilities in languages such as ECL, SQL, Python/Spark, or Java, as well as experience analyzing complex datasets. Familiarity with data governance, innovative data solutions, and effective communication with both technical and non-technical audiences are also important.

Yes, for the Senior Data Architect role, candidates must possess an active TS/SCI with Full Scope Polygraph (FSP) clearance. This ensures that the data you work with meets the required security standards and complies with mission obligations within federal or intelligence community contexts.

The salary range for the Senior Data Architect position is between $225,000 and $250,000, depending on various factors such as location, skills, and experience. In addition, we offer a comprehensive benefits package, including 100% company-paid health insurance, paid time off, a generous 401(k) retirement contribution, and incentive bonuses.

When answering this question, provide specific examples of data modeling projects you have led or contributed to. Discuss the methodologies you employed, the tools you used, and the impact of your work on operational outcomes. It's beneficial to illustrate how your designs supported complex analytics and operational requirements.

To answer this, share your strategies for ensuring data integrity, such as routine data validation processes, complex dataset analysis, and implementing data quality frameworks. Emphasize your commitment to improving data accessibility through clear documentation, user-centric data architecture, and collaboration with stakeholders.

Discuss your proficiency in relevant programming languages such as ECL, SQL, Python/Spark, or Java. Provide examples of how you have applied these languages in real-world projects, including any challenges you overcame and the results achieved.

Reflect on your experiences where you worked closely with mission stakeholders. Talk about how you engaged them to understand their data requirements and how you translated those into technical solutions. Highlight the importance of communication in ensuring all parties are satisfied with the final outcome.

In your response, detail your experience with data governance practices, such as data quality frameworks and lifecycle management. Explain how you have contributed to compliance with mission requirements and addressed challenges in maintaining high data standards.

Provide a specific example of a complex dataset you analyzed. Explain the steps you took to assess its quality, structure, and interconnectivity, and how this analysis led to insights that enhanced mission processes. Highlight the tools and methodologies you used.

Discuss your commitment to professional development and continuous learning. Mention specific resources such as online courses, webinars, or industry conferences that you follow to keep up with advancements in data architecture and analytics technologies.

Share a particular challenge you encountered, such as data integration from diverse sources or ensuring data security. Describe the steps you took to address it, the resources you employed, and the eventual outcome, focusing on lessons learned.

To answer this, talk about methodologies you use for designing scalable data models. You might discuss aspects like modular design, leveraging cloud technologies, or optimizing performance for increasing volumes of data – along with the importance of scalability in supporting future business objectives.

Explain your approach to data transformation, emphasizing best practices like maintaining data integrity, documenting transformation processes, and using automation tools. Detail how following these practices leads to reliable results that can be effectively utilized by stakeholders.