Senior Business Analyst

TriNet•Atlanta, GA

8d•Onsite

About The Position

As part of the Enterprise Data & Analytics team, the Senior Business Data Analyst – Planning & Modeling is responsible for leading functional analysis and conceptual solution design scenario planning, predictive modeling, and advanced analytics use cases. This role focuses on bridging business-driven modeling needs with technical implementation—partnering with Analytics and business teams to understand modeling methodologies, assumptions, and decisioning requirements, and translating them into scalable, reusable frameworks built on modern data platforms. Acting as the connective layer between model designers, end users, and engineering teams, this role ensures that models are not only analytically sound, but also operationalized as robust, scalable data solutions that drive consistent, enterprise-wide decision-making.

Requirements

Bachelor’s degree in Finance, Economics, Data Analytics, Information Management, Computer Science, or related field
6+ years in business analysis, data analysis, or modeling roles (financial, operational, or analytical).
Experience building scenario planning, forecasting, or predictive modeling frameworks.
Experience working across Analytics, Finance, and Engineering teams.
Experience with modern data platforms (data lakes, pipelines) preferred.
Strong expertise in scenario planning, forecasting, or predictive modeling.
Ability to translate business needs into model logic and technical requirements.
Strong understanding of driver-based modeling and dependencies.
Familiarity with data platforms, SQL, and data pipelines (ETL/ELT).
Strong communication skills across business and technical stakeholders.
Experience with BI tools (Power BI, Tableau) preferred.

Nice To Haves

Experience with modern data platforms (data lakes, pipelines) preferred.
Experience with BI tools (Power BI, Tableau) preferred.

Responsibilities

Partner directly with model designers and end users to understand how models are constructed, used, and drive decision-making.
Translate modeling approaches, assumptions, and workflows into clear functional requirements and structured logic for implementation by engineering teams.
Ensure alignment between intended model design, business usage, and technical implementation, maintaining fidelity as models are operationalized.
Act as the primary liaison between business stakeholders and engineering, ensuring models are accurately represented, scalable, and reusable within the data platform.
Lead requirements discovery and facilitation for modeling use cases.
Translate business needs into structured modeling logic and technical requirements (inputs, calculations, outputs).
Document assumptions, business rules, and dependencies to ensure transparency and reproducibility.
Ensure alignment of decisioning needs and intended model outcomes.
Define end-to-end model architecture, including: Inputs (source systems, drivers) Transformations and calculation logic Outputs (metrics, KPIs, reporting layers)
Analyze and document data flows from source → model → consumption.
Ensure alignment with data governance, lineage, and quality standards.
Design modeling frameworks and patterns prior to technical implementation.
Evaluate multiple approaches (e.g., centralized vs modular models, batch vs near real-time).
Identify edge cases, gaps, and constraints that impact model accuracy and usability.
Ensure solutions are extensible across multiple business domains and use cases.
Partner with data engineering to operationalize models in the data lake and pipelines.
Act as the functional lead through delivery, supporting build, validation, and UAT.
Ensure alignment between business stakeholders and engineering throughout delivery.
Perform model validation, reconciliation, and sensitivity analysis.
Continuously refine models based on business feedback and evolving assumptions.
Identify opportunities to standardize and scale modeling components.
Establish best practices for scenario planning and predictive modeling on modern data platforms.
Drive adoption of reusable modeling frameworks and patterns.
Raise the bar on functional clarity, model documentation, and design quality.