Data Scientist

Overview

This role focuses on designing, developing, and maintaining enterprise data infrastructure to support advanced analytics across engineering and business teams. The Data Engineer builds scalable data pipelines, ensures data integrity and accessibility, and enables reporting and dashboards that transform raw data into actionable insights. The position supports enterprise data architecture and governance, particularly across cloud platforms and business intelligence tools, while working closely with cross-functional stakeholders in a fast-paced engineering environment.

• Design, build, and maintain scalable data pipelines and data storage solutions to support enterprise analytics and reporting.
• Integrate data from multiple sources, including databases, APIs, and enterprise platforms, into centralized data warehouses or data lakes.
• Transform raw data into structured, analytics-ready datasets that support dashboards, reports, and advanced analytics use cases.
• Ensure data accuracy, consistency, and integrity through robust validation, quality control, and monitoring processes.
• Optimize data pipelines and storage solutions for performance, scalability, and reliability in cloud and on-premises environments.
• Support data architecture governance by identifying and maintaining authoritative sources of truth (ASOT) for key data domains.
• Enable and manage data replication, transformation, and synchronization across enterprise platforms such as AWS and Power BI.
• Collaborate with data scientists, analysts, engineers, and business stakeholders to gather requirements and translate them into technical data solutions.
• Develop and maintain data models and documentation that support enterprise reporting and analytics.
• Troubleshoot data issues, diagnose root causes, and implement corrective actions to maintain system performance and data quality.
• Work cross-functionally with engineering and stakeholder teams to align data solutions with program and business objectives.
• Apply strong analytical skills, including statistics, probability, and mathematical modeling, to support data-driven decision-making.
• Participate in Agile/Scrum activities and contribute to continuous improvement of data engineering processes and practices.
• Manage multiple priorities and projects in a fast-paced environment while maintaining high standards of quality and organization.

• Proven experience in data engineering and data infrastructure design, including pipelines, data warehouses, and databases.
• Strong proficiency in SQL and experience with Oracle databases.
• Hands-on experience with AWS and Redshift for cloud-based data solutions.
• Proficiency with Power BI for data transformation, reporting, and dashboard development.
• Experience in data pipeline development using ETL/ELT tools and frameworks.
• Practical experience with data validation, quality control, and data governance, including familiarity with authoritative sources of truth (ASOT).
• Strong analytical skills, including knowledge of statistics, probability, and mathematical modeling.
• Experience working cross-functionally with engineering teams and business stakeholders.
• Strong written and verbal communication skills, with the ability to explain complex data concepts to non-technical audiences.
• Ability to work both independently and collaboratively within team environments.
• Demonstrated experience translating data into actionable insights that support decision-making.
• Strong troubleshooting and problem-solving abilities in data and system environments.
• Ability to manage multiple priorities effectively in a fast-paced setting.
• High attention to detail and strong organizational skills.
• Experience developing data requirements and data models to support analytics and reporting.

• Experience with Midas, SSAS, Alation, and Dataiku for data management and analytics.
• Familiarity with MATLAB/Simulink, Mathcad, and Magic Draw in engineering and modeling contexts.
• Exposure to Agile/Scrum methodologies, including tools such as Jira or Service Now.
• Knowledge of combat vehicle systems or defense-related programs.
• Experience with structured problem-solving methodologies.
• Understanding of Agile metrics and reporting practices.
• Systems engineering knowledge or familiarity with engineering processes.

The position operates in a hybrid work model, with three days onsite and two days remote each week, providing a balance of in-person collaboration and focused remote work. The role follows a 9/80 schedule, offering every other Friday off while maintaining full-time hours. You will work in a fast-paced, cross-functional engineering environment alongside data scientists, analysts, and engineering teams, collaborating on complex, large-scale data systems that support advanced engineering programs.

The environment offers exposure to modern data tools and cloud technologies such as AWS and Power BI and provides opportunities to contribute to enterprise-level data strategy and…