Manager, Data Analytics & Revenue Intelligence

Manager, Data Analytics & Revenue Intelligence
Data-first business intelligence, revenue analytics, and commercial controls Department Data Analytics / Revenue Operations Employment Type Full-Time, Permanent Location Remote, US or Canada Reports To VP, Strategy & Operations About the Role
SkySys is seeking a highly analytical, technically hands-on Manager, Data Analytics & Revenue Intelligence to build the data foundation, analytics, reporting, and controls required to manage a complex global workforce and services business.

This role is primarily a data and business intelligence position with strong revenue and commercial responsibility. The person will collect, clean, transform, reconcile, model, and analyze data across multiple systems and convert it into reliable operational and financial intelligence for leadership.
The role will connect data across Sales, CRM, ATS, Operations, Service Delivery, payroll, EOR platforms, timesheets, billing, accounting, and client/VMS environments. The objective is to create a dependable single source of truth from client and account level down to country and individual resource level.

The ideal candidate is a strong data professional who also understands revenue, gross margin, P&L, pricing, workforce economics, and financial reconciliation. This is not a reporting-only role: the person must be able to trace discrepancies to source systems, automate recurring analysis, build exception-based controls, and explain what is driving changes in revenue, cost, margin, headcount, and account performance.

Role Focus 60-70% Data engineering-lite, analytics, BI, reconciliation, automation, data quality, and business intelligence 30-40% Revenue operations, pricing analytics, account P&L, billing controls, margin analysis, and commercial support Key Responsibilities
1. Data Integration, Transformation & Data Quality
Own the process of bringing together operational and financial data from multiple platforms into a consistent, usable analytical structure.

• Extract, combine, clean, transform, and normalize large datasets from CRM, ATS, payroll, EOR, timesheet, billing, accounting, VMS/MSP, and operational systems.
• Use SQL and data transformation tools to join datasets, create reusable logic, and reduce manual spreadsheet-based processing.
• Design and maintain standardized data mappings for clients, accounts, projects, service lines, countries, resources, currencies, bill rates, pay rates, and cost components.
• Identify duplicate, incomplete, inconsistent, stale, or conflicting records and trace issues back to their source.
• Create data validation rules, control checks, and exception reports that identify issues proactively.
• Partner with business owners to improve upstream data capture so recurring reporting problems are corrected at the source.
• Document data definitions, business rules, assumptions, calculation logic, and ownership.

2. Business Intelligence, Dashboards & Executive Analytics
Build reporting and analytics that allow leadership to quickly understand business performance and investigate the underlying drivers.

• Develop and maintain Power BI dashboards or equivalent BI solutions with automated or repeatable refresh processes.
• Create drill-down reporting from company -> client -> account -> country -> resource level.
• Build reporting for revenue, gross profit, gross margin %, headcount, utilization, revenue per resource, cost trends, unbilled revenue, billing exceptions, margin leakage, and account profitability.
• Analyze trends, variances, outliers, cohorts, and drivers rather than only presenting static totals.
• Create executive-level scorecards and recurring management reporting with clear commentary on what changed, why it changed, and what action is recommended.
• Enable self-service access to trusted metrics while maintaining consistent definitions and governance.

3. Single Source of Truth & Data Model

• Create and maintain a trusted commercial and operational data model spanning Sales, Operations, Finance, Payroll, Service Delivery, and Talent/Recruiting.
• Build a consistent hierarchy linking Client -> Account -> Project/SOW -> Country -> Resource -> Revenue -> Cost -> Gross Margin.
• Define master-data standards and unique identifiers that allow records to be matched across systems.
• Reduce dependence on disconnected spreadsheets by developing structured datasets and repeatable analytical processes.
• Maintain historical snapshots where required so changes in rates, headcount, costs, and account economics can be analyzed over time.

4. Revenue, Billing & Payroll Reconciliation
Use data-driven controls to reconcile the full commercial lifecycle and identify financial leakage.

• Reconcile active resources -> approved timesheets -> payroll/pay rates -> loaded costs -> client billing -> recognized revenue.
• Validate that every active billable resource is billed accurately and that terminated or inactive resources stop generating payroll or billing at the appropriate time.
• Identify missing billing, underbilling, overbilling, incorrect bill rates, incorrect pay rates, duplicate billing, missing timesheets, incorrect dates, unapproved expenses, and unbilled resources.
• Reconcile invoices against contractual rates, purchase orders, approved timesheets, active resource rosters, and payroll data.
• Develop automated or exception-based reconciliation routines so teams investigate variances rather than manually reviewing every record.
• Track unbilled and deferred revenue where applicable and provide clear aging and root-cause visibility.

5. Account P&L, Gross Margin & Profitability Analytics

• Maintain and analyze account-level and resource-level profitability datasets.
• Calculate actual revenue, direct labor cost, employer cost, subcontractor/EOR cost, expenses, gross profit, and gross margin.
• Analyze gross margin by account, client, resource, country, service line, worker type, and other relevant dimensions.
• Compare quoted margin vs. actual realized margin and quantify margin leakage.
• Investigate the root causes of margin changes, including rate changes, overtime, FX, benefits, statutory costs, EOR fees, expenses, non-billable time, and data errors.
• Identify underperforming accounts and provide actionable recommendations to improve profitability.

6. Pre-Sales Pricing & Commercial Analytics

• Partner with Sales, Solutions, Service Delivery, and Finance on new opportunities, renewals, and expansions.
• Develop and maintain bill-rate, pay-rate, loaded-cost, and gross-margin models.
• Incorporate employer taxes, statutory contributions, benefits, PTO/holiday costs, EOR fees, insurance, bonuses, allowances, FX, and country-specific employment costs.
• Model pricing across employees, independent contractors, subcontractors, and EOR arrangements.
• Perform scenario and sensitivity analysis for pricing, rate increases, discounts, rebates, payment terms, and other commercial concessions.
• Maintain standardized pricing calculators, assumptions, and minimum margin thresholds.

7. Forecasting & Decision Support

• Support revenue, headcount, cost, and gross-margin forecasting using current run-rate data, pipeline, attrition, planned starts/ends, and account expansion assumptions.
• Analyze account and workforce trends to identify emerging risks or opportunities.
• Build analytical models that improve forecast accuracy over time.
• As data maturity improves, support predictive analysis around attrition, revenue, account growth, utilization, and margin performance.
• Provide ad hoc analysis for executive leadership, major client negotiations, investment decisions, and operational changes.

8. Revenue Assurance, Audit & Controls

• Build a formal Revenue Assurance framework supported by data, automated checks, and exception reporting.
• Establish monthly account financial close and reconciliation routines.
• Perform recurring audits of active resources, rates, employment costs, timesheets, payroll, invoices, POs, expenses, and terminated resources.
• Identify control gaps that could create revenue leakage, overpayment, underbilling, or incorrect commissions.
• Maintain transparent audit trails supporting financial and operational calculations.
• Measure the financial impact of identified exceptions and track resolution through closure.

Technical & Analytical Requirements

• Strong SQL skills required, including joins, aggregations, CTEs, data validation, reconciliation logic, and working with large relational datasets.
• Strong Power BI experience required, including data modeling, Power Query, DAX, dashboard development, drill-down reporting, and refresh management; Tableau or comparable BI expertise may be considered.
• Advanced Excel required, including complex formulas, pivots, Power Query/data transformation, reconciliation models, and large-data analysis.
• Experience integrating and reconciling data from multiple business systems such as CRM, ATS, ERP/accounting, payroll, EOR, timesheet, billing, and VMS/MSP platforms.
• Experience with ETL/ELT concepts, APIs, database structures, data warehouses/lakes, or automation tools is strongly preferred.
• Experience with Python, R, dbt, Alteryx, Fabric, Snowflake, BigQuery, Azure data tools, or similar analytics/data platforms is a plus, but not required if the candidate is strong in SQL and BI.
• Ability to design practical data models and reusable analytical logic without requiring a large engineering team.
• Strong understanding of data governance, data quality, master data, and metric definitions.

Business & Financial Qualifications

• 5+ years of experience in Data Analytics, Business Intelligence, Revenue Analytics, Financial Analytics, Commercial Analytics, Revenue Operations, or a similar data-intensive role.
• Demonstrated experience manipulating, reconciling, and analyzing complex datasets from multiple systems.
• Strong understanding of revenue, gross profit, gross margin, P&L, cost structures, pricing, and financial reconciliation.
• Ability to translate business questions into data requirements, analysis, dashboards, and actionable recommendations.
• Strong problem-solving skills and ability to investigate discrepancies from executive summary level down to transaction/resource-level detail.
• Ability to communicate complex analytical findings clearly to Finance, Operations, Sales, Service Delivery, and senior leadership.
• Comfort operating in a fast-moving environment where source data may initially be fragmented, inconsistent, or incomplete.

Preferred Industry Experience
Experience within one or more of the following is strongly preferred:
Preferred backgrounds include IT Staffing / Workforce Solutions; Professional Services / Consulting; Managed Services; Employer of Record / Global Employment; MSP/VMS environments; and multi-country, multi-currency service businesses.
Experience analyzing resource-based revenue models, global workforce costs, and multi-currency commercial data is particularly valuable.
Key Performance Indicators • Data Accuracy & Completeness • Data Reconciliation Accuracy • Dashboard / Reporting Timeliness • Manual Reporting Reduction & Automation • Data Quality Exceptions Identified and Resolved • Revenue Leakage Reduction • Billing Accuracy • Payroll-to-Billing Reconciliation Accuracy • Unbilled Revenue Reduction • Account P&L Accuracy • Quoted vs. Actual Gross Margin Variance • Gross Margin Improvement • Forecast Accuracy • Time to Identify and Resolve Data / Financial Exceptions • Adoption of Standardized Data and Reporting Across Teams