Financial Data Analyst

Siemens•Peachtree Corners, GA

6h•$66,973 - $114,811•Remote

About The Position

The Financial Data Analyst will own and coordinate the financial forecasting process for annual and periodic planning cycles. This role serves as the analytical bridge between Project Strategy & Planning, Operations, and Finance by transforming capacity plans, production schedules, customer demand, backlog, product mix, pricing, and historical performance into meaningful business forecasts and insights. This position plays a critical role in providing visibility into factory performance, bookings, customer mix, product mix, and revenue outlooks across the our business. The analyst will help leadership understand where the business is performing today, where it is expected to perform tomorrow, and what operational factors are driving those results. The ideal candidate is a highly analytical problem solver who enjoys working with large datasets, finding patterns, validating assumptions, and translating complex information into clear recommendations. This is primarily a data and analytics role with a strong focus on forecasting and business insights.

Requirements

Bachelor's degree in Data Analytics, Business Analytics, Finance, Economics, Supply Chain, Engineering, Operations, Mathematics, Statistics, Business, or 4 years equivalent experience in lieu of a bachelor’s degree.
3+ years of experience in data analytics, forecasting, business intelligence, operations analytics, manufacturing analytics or financial analysis.
Advanced Microsoft Excel skills including complex formulas, pivot tables, data modeling, reconciliations, and large dataset analysis.
Strong analytical and problem-solving skills with the ability to work through ambiguity.
Experience building dashboards, reports, and executive-level presentations.
Ability to translate detailed data into actionable business recommendations.
Strong communication and stakeholder management skills.
Ability to manage multiple priorities and deadlines in a fast-paced environment.
Willingness to travel up to approximately 5% yearly.

Nice To Haves

Experience with Tableau, Power BI, SAP, KNIME, SQL, Smartsheet, Power Query, Power Automate, Python, or similar analytical tools.
Experience in manufacturing, project-based, engineer-to-order, or configure-to-order environments.
Knowledge of capacity planning, production scheduling, backlog management, customer demand analysis, and operational forecasting.
Lean, Six Sigma, or continuous improvement experience.

Responsibilities

Own and coordinate business forecasting activities, developing monthly bookings, volume, and revenue forecasts using capacity plans, production schedules, backlog, customer demand, historical performance, and other business inputs.
Support customer, product, factory, and business-level forecasting while partnering with stakeholders to ensure assumptions accurately reflect current business conditions.
Analyze forecast changes, identify key business drivers, and communicate risks and opportunities impacting performance.
Create and maintain reporting that provides visibility into customer mix, product mix, factory loading, demand trends, and overall business performance.
Analyze large datasets to identify trends, risks, opportunities, and performance drivers through scenario modeling and sensitivity analysis.
Develop data-driven recommendations and translate complex analysis into clear, actionable insights for leadership and cross-functional teams.
Validate and reconcile data across schedules, operational reports, ERP systems, dashboards, and financial outputs to ensure forecast accuracy and consistency.
Investigate data discrepancies, forecast variances, and reporting inconsistencies while maintaining documentation, assumptions, and controls that support transparency and data integrity.
Establish and maintain trusted, repeatable forecasting processes and a reliable source of business performance data.
Develop dashboards, management reports, trend analyses, and executive-level presentations that support business planning and decision-making.
Standardize forecasting tools, templates, and reporting structures while improving data quality, reporting efficiency, and forecast visibility.
Automate recurring reporting, validation, and data preparation activities and partner with digital and analytics teams to enhance forecasting capabilities and reduce manual effort.