Finance Systems Manager

RingCentral•Belmont, CA

About The Position

We are seeking a Finance Systems Manager with deep experience in financial analytics, business intelligence, and enterprise reporting. This role will serve as a strategic and hands-on leader responsible for building, governing, and scaling a modern finance analytics ecosystem. The ideal candidate brings strong advanced technical expertise, and the ability to partner closely with Finance, FP&A, and Engineering stakeholders.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Analytics, or a related field
8+ years of experience in Financial Analytics, Business Intelligence, or Data Analytics, with significant leadership experience
Advanced expertise in SQL for analytics-ready data modeling
Strong experience with Python for data transformation, analysis, and forecasting
Hands-on experience with Tableau (primary) and other BI tools
Strong understanding of financial and transactional data domains
Proven ability to define, govern, and scale financial metrics across the enterprise
Excellent communication and stakeholder management skills, with executive-level presence
SQL & Data Warehousing Advanced SQL skills for analytics, data modeling, and performance optimization
Strong hands-on experience with Snowflake (preferred) or similar cloud data warehouses
Experience building analytics-ready tables, aggregations, and reusable views
Ability to implement data quality checks, reconciliations, and audit controls in SQL
Python & Advanced Analytics Strong proficiency in Python for data transformation and analysis
Experience using Python libraries such as pandas, NumPy, and statsmodels (or equivalent)
Ability to implement business logic, data pivots, trend analysis, and lightweight forecasting
Experience handling missing data, outliers, and large datasets efficiently
BI & Visualization Tools Deep experience with Tableau (primary BI tool), including: Dashboard design for executive and finance audiences Performance optimization and best practices Semantic layers and governed metrics Exposure to Power BI or similar BI tools is a plus
Data Pipelines & Analytics Engineering Experience working with end-to-end analytics pipelines from source systems to dashboards
Familiarity with ETL / ELT concepts and tools (e.g., Tableau Prep, dbt, or similar frameworks)
Ability to partner effectively with Data Engineering teams on scalable data architecture
Financial & Analytics Systems – Good to have Experience working with financial and transactional data (revenue, billing, payments, forecasting)
Familiarity integrating data from ERP, revenue, or billing systems into analytics platforms
Strong understanding of metric governance and financial data consistency

Nice To Haves

Background in fintech, banking, SaaS, or large-scale transactional environments
Experience working with high-volume, customer-level financial data
Prior experience leading enterprise-wide BI or financial transformation initiatives

Responsibilities

Data Pipeline & Architecture Ownership
Oversee end-to-end finance analytics pipelines, including: SQL-based data extraction and aggregation Python-based transformation, business logic, and forecasting BI semantic layers and dashboard consumption AI-enabled analytics and self-service insights
Ensure performance, scalability, and reliability of financial reporting datasets
Advanced Analytics & Insights Delivery
Lead Python-driven analytics such as: Dataset reshaping and pivots for reporting and analysis Handling missing data, outliers, and data quality issues Trend analysis and customer-level performance monitoring Lightweight forecasting and scenario analysis for planning
Translate complex financial data into clear, actionable insights for executive leadership
BI & Executive Reporting
Design, govern, and optimize executive dashboards and KPI frameworks using Tableau and other BI platforms
Ensure dashboards support intuitive drill-downs across customer, product, segment, region, and time
Drive consistency between dashboards, reports, and AI-driven insights to maintain metric trust
AI & Analytics Enablement
Support AI-driven finance analytics initiatives by ensuring: AI tools use governed, approved financial metrics Outputs are explainable, traceable, and auditable Role-based access and data security controls are enforced
Establish guardrails to prevent incorrect metrics or misleading interpretations
Operational Excellence
Drive continuous improvement in BI workflows, SQL performance, Python pipelines, and automation
Improve efficiency, accuracy, and scalability of financial reporting systems
Ensure smooth transitions between legacy and next-generation financial platforms