Print Supplies Big Data Analyst

HP•Vancouver, WA

4d•$105,050 - $161,800

About The Position

The Print Supplies Big Data Analyst supports HP’s Print Supplies business at the intersection of analytics, data quality, product data stewardship, monthly reporting operations, and forecasting enablement. The role partners with data engineers, data scientists, forecasting teams, and business stakeholders to maintain data-processing pipeline integrity, manage key business reference and enrichment data, and automate data verification and acceptance processes. The position applies advanced subject-matter knowledge and independent judgment to complex business and data issues. It contributes expertise to functional and cross-functional projects, develops productive working relationships, and provides guidance and mentoring to less-experienced employees. The work involves complex problems requiring in-depth evaluation of multiple factors.

Requirements

Four-year or graduate degree in Mathematics, Statistics, Economics, Computer Science, or a related discipline, or commensurate work experience or demonstrated competence.
Typically 7–10 years of work experience, preferably in data analytics, database management, statistical analysis, or a related field.
Experience with exploratory analysis, root-cause analysis, metric design, data validation, anomaly detection, and diagnosing complex data issues across multiple factors.
Knowledge of data profiling, anomaly detection, metric validation, documentation, data lineage, privacy awareness, and communicating data-quality risks and remediation paths.
Experience with Power BI, Qlik, advanced Excel, or similar tools for dashboards, analyses, and presentations.
Python skills for data analysis, validation, automation, time-series analysis, reproducible workflows, and recurring quality-check and investigation tooling.
SQL skills, including complex joins, CTEs, window functions, and query performance optimization.
Ability to work with Databricks and modern analytics tooling, including notebooks, queries, dashboard-supporting datasets, and AI-assisted development environments while maintaining reproducible and reviewable outputs.
Ability to support forecasting partners, interpret restatements and metric volatility, and contribute to forecasting automation or time-series analysis workflows.
Knowledge of applied statistics, including sampling, confidence intervals, and basic experiment or pre/post evaluation, as applicable to the role.
Strong documentation practices for code, processes, analyses, investigations, data lineage, and stakeholder guidance.
Familiarity with version control such as Git, Jira, and Confluence.
Cross-organizational skills: effective communication, results orientation, learning agility, digital fluency, and customer centricity.

Nice To Haves

Experience in data analytics, database management, statistical analysis, or a related field is preferred.
Experience with applied statistics such as sampling, confidence intervals, and basic experiment or pre/post evaluation is role-dependent.

Responsibilities

Own and support monthly validation processes for print telemetry outputs, dashboard semantic layers, and related usage/share reporting handoffs.
Partner with Forecasting and business stakeholders to analyze usage, share, installed base, product readiness, and release approval questions, communicating findings clearly.
Develop and maintain automated, documented processes for recurring validation, issue diagnosis, data quality checks, and stakeholder decision-making.
Conduct root-cause analysis of discrepancies across sources, grains, tables, dashboards, and processing layers, including mismatches related to definitions, filters, platform coverage, or reporting timing.
Provide consultative support for quick-turn ad hoc requests, escalations, and deep-dive investigations, and help route work through appropriate intake, prioritization, and transition processes.
Collaborate with dashboard, data engineering, and business intelligence partners to improve analytics-output transparency, repeatability, and maintainability.
Provide guidance and mentoring to less-experienced staff and lower-level employees.
Apply advanced subject-matter knowledge, lead and contribute to complex projects, exercise independent judgment in resolving business issues, and help establish policies.
Develop productive internal and external working relationships.