Analytics Engineer, Quality Analytics

As a Data Engineer for Quality Analytics, you are a technical problem-solver who bridges the gap between our raw data systems and critical manufacturing business insights. You will work within existing business systems (ERP, MES, Inventory Management, Warehouse Management, Quality Incident management system and QMS systems) to build, maintain, and improve the data analysis models and pipelines that power our quality analytics. This role combines data pipeline development with deep-dive investigation into data quality as it relates to manufacturing quality. You will be responsible for not just building the data infrastructure, but also for troubleshooting sources of variance in the data and implementing technical solutions to improve data quality at its core. If you love solving complex data puzzles that have a direct impact on real-world manufacturing quality, this role is for you.

WHAT YOU’LL DO

• Support & Maintain Data Analytics: Support basic analytics and investigation utilizing existing tools and capabilities. Direct users to utilize analytic dashboards, troubleshoot existing dashboard accuracy, and provide modification & improvements to increase dashboard effectiveness.
• Investigate Data Quality: Act as a lead technical investigator for data quality issues. When a dashboard is inaccurate or data seems wrong, you will perform deep-dive analysis using SQL and Python to trace the problem back to its source and identify the root cause.
• Troubleshoot & Optimize: Support the analytics team by troubleshooting data access issues, improving pipeline performance for faster dashboard loads, and ensuring the overall health and reliability of the quality data ecosystem in Foundry.
• Drive Technical Improvements: Implement robust data quality checks, validation rules, and automated monitoring directly within the data pipelines to proactively prevent future data quality issues and eliminate variance.
• Enable Self-Service Analytics: Partner closely with the cross-functional teams and ML Engineers to understand current and future data needs. Build clean, reliable, and well-structured datasets that allow teams to independently create reliable dashboards, reports, and models.
• Lead Data Projects: Collaborate with partner teams for analytics initiatives from requirements gathering through deployment and explore efficient ways to deliver the ask. Partner with Data Scientists and other analysts to deliver high impact, well rounded solutions.
• Document the Data Landscape: Develop and maintain clear documentation for key data sources, transformation logic, and data models to ensure a shared understanding across the team.

REQUIRED QUALIFICATIONS

• Bachelor’s degree in Data Analytics, Engineering, Computer Science, or a related technical field.
• Experience in Engineering, Manufacturing, or Quality in defense products and/or complex production process management.
• 3+ years of experience in a technical data role such as Data Engineer, Analytics Engineer, or BI Engineer.
• Proven experience building and maintaining data pipelines and/or analytics models in a platform like Palantir Foundry, Databricks, or a similar cloud environment.
• Proficiency in SQL for querying and analyzing large, complex datasets across multiple systems.
• Proficiency in Python for data transformation and scripting (e.g., using Pandas, PySpark).
• Strong analytical mindset with a demonstrated ability to perform root cause analysis on complex data issues.
• Great at working cross-functionally with other teams/departments, able to communicate in both technical and non-technical contexts.
• Experience with software tools/services like Slack, Jira, Excel, and Confluence.
• Eligible to obtain and maintain an active U.S. Secret security clearance.

PREFERRED QUALIFICATIONS

• 3+ years of experience in a data coordinator, data analysis, quality analytics, or business intelligence role.
• Direct experience conducting research and analyzing data to gain insights into the business.
• Experience building or supporting analytics for complex hardware manufacturing operations, including new product introduction (NPI), ramp-up, and high-volume production phases.
• Experience with dashboarding and data visualization tools (e.g., Foundry Workshop/Quiver, Tableau, PowerBI) and understanding how data models impact their performance.
• Familiarity with quality methodologies and concepts like RCCA or 8D, GD&T, FMEA, IQC/OQC, and how data supports them.
• Exceptional cross-functional collaboration and communication skills.
• Experience with software development best practices and SDK patterns, including version control with Git.
• Able to work with minimal supervision, taking ownership of work and completing tasks in a timely manner, while adapting rapidly to changing work environments, priorities and organizational needs.