Big Data Laser Analyst

We are seeking a highly analytical, business-oriented Data Analyst to support the Print Supplies business. In this role, you will translate consumer and enterprise print telemetry into trusted metrics, dashboards, and actionable insights, with a primary focus on laser printer data. You will partner closely with data engineers and data scientists to improve data reliability, define KPI and metric semantics, and influence cross-functional decisions through clear data storytelling.

We are seeking a highly analytical, business-oriented Data Analyst to support the Print Supplies business. In this role, you will translate consumer and enterprise print telemetry into trusted metrics, dashboards, and actionable insights, with a primary focus on laser printer data. You will partner closely with data engineers and data scientists to improve data reliability, define KPI and metric semantics, and influence cross-functional decisions through clear data storytelling.

• Own laser print telemetry business insights for the Print Supplies organization, from problem framing and metric definition through analysis, visualization, and executive-ready recommendations.
• Partner with stakeholders to clarify goals, translate business questions into analytical requirements, define critical KPIs, and deliver insights that drive decisions.
• Provide consultative support and ad hoc analyses with clear hypotheses, methods, and assumptions; produce reusable queries, datasets, and shareable outputs with documented limitations.
• Collaborate with internal and external partners to align on telemetry instrumentation, data collection methodology, and platform differences to ensure consistent interpretation across printer platforms.
• Investigate data quality issues (completeness, accuracy, timeliness), perform root-cause analysis across pipelines and sources, and communicate impacts and remediation paths with appropriate caveats.
• Enable self-service analytics by documenting dashboards and KPI definitions, and coach users on correct interpretation and common pitfalls.
• Design and maintain measurement frameworks (e.g., adoption, usage, retention, share, funnel/driver analyses); when applicable, support experiment or pre/post evaluation and interpret results for stakeholders.
• Create clear narratives through written summaries, presentations, and recurring reporting that highlight trends, drivers, risks, and recommended actions for leadership and partner teams.
• Provide guidance and mentoring to less- experienced staff members.
• Solve difficult and complex problems with a fresh perspective, demonstrating good judgment in selecting creative solutions and managing projects independently.
• Lead moderate to high complexity projects, delivering professionally written reports, and supports the realization of operational and strategic plans.

• Four-year or Graduate Degree in Mathematics, Statistics, Economics, Computer Science, or any other related discipline or commensurate work experience or demonstrated competence.
• Typically has 4-7 years of work experience, preferably in data analytics, database management, statistical analysis, or a related field or an advanced degree with 3-5 years of work experience.

• Programming Language/s Certification (SQL, Python, or similar)

• Analytics: exploratory analysis, root-cause analysis, and KPI design
• SQL: complex joins, CTEs, window functions, query performance optimization
• Python (and/or PySpark) for analysis, data validation, and automation. Familiar with VSCode and Databricks Ideally
• Ability to interpret telemetry and pipeline outputs in business context, including communicating uncertainty and known limitations
• Comfort working across multiple data sources/layers and explaining differences in source-of-truth, grain, and transformations
• Comfort with modern analytics tooling (including AI-assisted development) while maintaining reproducibility and reviewable outputs
• Dashboarding & data visualization:
  Power BI, Tableau, Qlik, advanced excel / PowerPoint or similar; ability to build stakeholder-ready reporting
• Data quality and governance: profiling, anomaly detection, metric validation, and documentation
• Strong documentation habits for metric definitions, data lineage, and stakeholder guidance; familiarity with version control (e.g., Git) a plus. Familiar with Jira and Confluence.
• Applied statistics: sampling, confidence intervals, and basic experiment/pre-post…