Principal Data Engineer / Financial Analyst

Job description

*Red Hat will not be providing visa sponsorship for this position. Therefore, in order to be considered for this position, you must have the ability to work without a need for current or future visa sponsorship.* About the Job Red Hat’s Global Sales Go-To-Market Strategy, Incentives & Data Analytics team is seeking a Principal Data Scientist / Financial Analysis professional to lead efforts to improve the accuracy, consistency, and strategic use of revenue, sales, and financial data across the organization. In this role, you will apply advanced analytics, data science, and financial expertise to develop key revenue performance measures, support precise business planning, and deliver trusted insights across the end-to-end revenue lifecycle. You will analyze bookings, recurring revenue, renewals, expansion, contraction, customer retention, product performance, and other revenue drivers to help business leaders understand historical results, assess in-quarter performance, and improve future revenue outcomes. This strategic position is responsible for designing scalable analytical and data-validation frameworks, automating data-quality and financial-reconciliation processes, and proactively identifying financial risks, revenue anomalies, and growth opportunities. You will help establish governance-grade reporting standards that provide stakeholders with accurate, timely, consistent, and auditable information. The successful candidate will combine deep data science and analytical expertise with strong commercial and financial acumen. You will play a pivotal role in advancing automation, standardization, predictive analytics, financial analysis, and visibility across global sales, revenue, planning, and incentive processes. You will serve as a trusted advisor to Sales, Finance, Operations, and executive stakeholders, translating complex financial and operational data into actionable insights that support revenue growth, forecasting, resource allocation, and strategic business decisions. What You Will Do Lead cross-functional initiatives to improve the accuracy, timeliness, completeness, and consistency of sales, revenue, and financial datasets used for planning, forecasting, performance measurement, and management reporting Develop advanced analytical models to evaluate revenue performance, including bookings, recurring revenue, renewals, retention, expansion, contraction, upsell, cross-sell, new business, and product- or customer-level revenue trends Analyze historical and in-quarter revenue performance to identify trends, performance gaps, financial risks, growth opportunities, and the underlying drivers of variance against plan, forecast, and prior-period results Build predictive and diagnostic models that improve revenue forecasting, customer-retention analysis, pipeline-conversion insights, and the identification of revenue at risk Partner with Finance and business-planning teams to reconcile operational revenue measures with financial results, ensuring alignment among sales activity, bookings, revenue recognition, forecasts, and management reporting Design and implement data-validation rules, pipeline-reconciliation dashboards, anomaly-detection models, and automated controls to ensure accounting-grade data integrity Establish automated checks to identify missing transactions, duplicate records, classification errors, unexpected revenue movements, product-mapping issues, timing differences, and inconsistencies across source systems Enforce data-submission calendars, lead recurring data-health reviews, and establish monthly and quarterly lock processes to maintain trusted, controlled, and auditable data workflows Standardize revenue definitions, classifications, financial measures, and business rules through collaborative reviews with Go-To-Market, Finance, Sales, Operations, and Incentives stakeholders Develop daily, weekly, monthly, and quarterly dashboards that surface revenue trends, performance anomalies, forecast risks, and key financial insights for business and executive stakeholders Perform variance analysis across actuals, targets, forecasts, and prior-period performance, clearly explaining the financial and operational drivers behind material changes Support annual and quarterly planning by developing analytical datasets and models for revenue targets, quota allocation, territory planning, capacity analysis, incentive design, and financial forecasting Curate and validate sales opportunity, bookings, customer, product, contract, and revenue data for use in incentives, quota modeling, territory design, performance reporting, and forecast-accuracy improvements Partner with Finance, Sales Operations, and reporting teams to develop trusted key performance indicators and scalable analytical tools supporting revenue planning, attainment, productivity, retention, and growth Apply statistical techniques and machine-learning approaches, where appropriate, to improve forecasting, segmentation, risk identification, pattern detection, and business-decision support Develop scenario and sensitivity analyses to help leaders assess the potential financial impact of changes in pipeline, conversion rates, customer retention, pricing, product mix, sales capacity, and market conditions Translate complex analytical and financial findings into clear executive-level insights, recommendations, and narratives that explain what happened, why it happened, and what actions should be taken Drive the digitization and automation of manual processes to reduce operational inefficiencies, strengthen financial controls, and improve scalability across global reporting and planning workflows Maintain data-governance standards by supporting compliance with internal controls, accounting requirements, audit expectations, data-lock procedures, and documented review processes Serve as a subject matter expert for revenue analytics and financial analysis, ensuring alignment on data use cases, financial definitions, reporting expectations, analytical methodologies, and governance timelines Mentor analysts, data scientists, and business partners on analytical methods, data-quality practices, financial interpretation, and the development of scalable, production-ready solutions What You Will Bring 10+ years of experience in data science, advanced analytics, business intelligence, financial analysis, sales operations, revenue operations, or a related field, preferably within a global technology or subscription-based business Demonstrated experience leading data-validation frameworks and developing business-ready datasets, analytical models, and scalable solutions that support strategic planning, forecasting, and executive decision-making Strong understanding of revenue and financial concepts, including bookings, recurring revenue, revenue recognition, retention, expansion, contraction, annual contract value, forecast variance, growth rates, profitability, and financial-performance measurement Experience analyzing revenue performance across products, customers, regions, sales segments, channels, and reporting periods to identify key business drivers and actionable opportunities Strong knowledge of financial planning and analysis practices, including actual-versus-plan analysis, forecast development, scenario modeling, sensitivity analysis, and executive financial reporting In-depth understanding of the sales and customer lifecycle, including pipeline generation, opportunity conversion, contracting, bookings, renewals, expansion, territory planning, quota setting, and incentive modeling Advanced proficiency in SQL and Python or R, with experience using statistical analysis, predictive modeling, machine learning, data transformation, and automation techniques Experience working with modern cloud data platforms, large-scale data environments, data pipelines, semantic models, and analytical development practices Proficiency with data-visualization and busine