Senior Data Analyst

Responsibilities

• Develop automated reports, dashboards, and analyses that surface KPIs, performance drivers, and trends.
  • In direct support of strategic or operational decisions.
• Build and manage data pipelines integrating structured and unstructured data sources across K2's enterprise systems.
  • Design and implement automated workflows using SQL, Python, Snowflake, and related tools to standardize data, minimize manual error, and uphold data integrity and governance best practices.
• Develop and maintain data retrieval processes and application integrations across multiple systems and data sources.
  • Partner with internal stakeholders across Manufacturing, Finance, Propulsion, Materials, and other disciplines to analyze drivers impacting production outputs and operational scaling.
• Support regulatory, financial, and accounting reporting by compiling and validating the underlying data.
  • Develop, maintain, and distribute recurring reports for Manufacturing, Finance, and Production teams, accurately and on schedule.
• Evaluate existing processes around data quality control, and report distribution.
  • Identify inefficiencies and deliver concrete improvements.
• Own documentation for data tools, analyses, and workflows; keep it current and usable.
  • Stay current on advancements in data analytics, automation, AI/ML, and BI tooling.

Qualifications

• Bachelor's degree in Data Science, Computer Science, Engineering, Mathematics, or a related STEM discipline.
• 6+ years of experience in a data analyst or analytics role.
• Proficiency in SQL and Python.
• Working experience with Snowflake and a modern BI platform (PowerBI or Tableau).
• Experience working with relational databases and building complex data visualizations.
• Ability to translate ambiguous business questions into structured analyses and clear recommendations.
• Comfortable modifying established workflows and troubleshooting data issues as they arise.

Nice to Have

• Experience with ETL processes and workflow automation.
• Background in manufacturing, finance, or operational data domains.
• Strong knowledge of financial reporting processes.
• Familiarity with physical manufacturing workflows.
• Experience mentoring or informally leading analysts on a data team.