Under the direct supervision of the Chief Technology Officer, the Principal Data Engineer is a senior member of the Data Engineering team. The RS21 Data Engineering division consists of data engineers and data architects who are dedicated to developing best-in-class data platforms to help solve some of today’s most challenging business problems. They work collaboratively with the other RS21 functional divisions to develop visually compelling, high performing, and deeply insightful solutions to help business stakeholders make critical decisions based on trustworthy data, presented in a way to highlight meaningful insights.

Responsibilities:

• Develops and oversees development of production-quality analytics databases, data lakehouses, and data warehouses.
• Designs and develops data integration strategies to ensure the right data into the data solutions to support the data analytics use cases.
• Mentors a team of data engineers and data architects to build the skills required to work effectively with business stakeholders to derive functional and technical requirements from business objectives and needs.
• Works collaboratively with other functional leads supporting Data Analysis, Data Science, Software Development, and UX/UI, to create and execute on a common technical vision.
• Identifies best practices regarding data collection, storage, integration, warehousing, and lifecycle management. Develops standards and procedures to ensure those best practices are known and implemented consistently.
• Designs and deploys data models (conceptual, logical, and physical) to specifically address client business needs.
• Works with project managers to identify and allocate data engineering and data architecture resources based on project deliverables and priorities.
• Meets with prospective customers and contributes to proposal development, initial project planning, and project cost estimation.

Qualifications:

• Bachelor’s degree in computer science, or relevant field with 8 years of experience developing ETL/ELT pipelines, constructing data models, and building data warehouses.
• Excellent leadership skills, written and verbal communication skills, and experience interfacing with prospective or existing business stakeholders.
• Demonstrated ability to accept strategic guidance from leadership and transform that guidance into an actionable plan with a clear path of execution.
• Experience mentoring technical teams.
• Experience managing an enterprise-level data solution through the entire software lifecycle.
• Experience gathering requirements, deriving functional and technical specifications, and defining and prioritizing the necessary tasks needed to fulfill those requirement and specifications according to the project’s period of performance (PoP).\
• Experience working in an Agile environment and participating in Agile ceremonies.
• Experience defining and implementing data governance rules to address business needs, regulatory requirements, and data sensitivity.
• Ability to analyze source data for potential data quality issues.
• Expert SQL and/or SQL-based languages and experience designing and implementing SQL and/or OLAP databases.
• Demonstrated understanding of data security best practices and experience integrating data security safeguards to prevent accidental and/or intentional privacy breaches and data misuse.

Nice to Have:

• Familiarity with Large Language Model integration and insight with respective industry trends for its future within Enterprises including the latest Data Engineering approaches.
• Experience working with state, local, and federal agencies.
• Ability to obtain a U.S. Government Security Clearance
• Supervisory experience leading a team of data engineers, architects, analysts, and/or software developers.
• Experience using NoSQL databases in production systems.
• Data mapping between source systems, data warehouses, and data marts.
• Prior experience implementing business rules via stored procedures, middleware, or other technologies.
• Familiarity with developing and implementing data extraction procedures from other systems.
• Develop or maintain standards, such as organization, structure, or nomenclature, for the design of data warehouse elements, such as data architectures, models, tools, and databases.
• Create supporting documentation, such as metadata and diagrams of entity relationships, business processes, and process flow.
• Create plans, test files, and scripts for data warehouse testing, ranging from unit to integration testing.