Apptio Configuration Lead - Lead Data Product Management Consultant

Wells Fargo & Company•Charlotte, NC

2d•Hybrid

About The Position

Wells Fargo is seeking a Lead Data Product Management Consultant to be part of the Technology Finance Shared Services Team. The Technology Finance Shared Services Team supports systems, data and processes that are utilized across Technology Finance. You will work hands on in the Apptio tool suite, primarily in Cost Transparency, Studio and Cloudability; to enable technology cost transparency, planning, and attribution. In partnering with senior TBM analysts and stakeholders, you will support Technology Allocations and chargeback, Technology Cost Transparency, and overall Cloudability support for our Tech Finance and FinOps Tech teams. This role is ideal for someone with a strong data mindset who enjoys problem solving, exploring analytics, and building repeatable, trusted outputs. It is also well suited for someone who is curious about practical ways to apply AI and automation to improve how work gets done.

Requirements

5+ years of data product or data management experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.

Nice To Haves

Advanced hands-on experience with Apptio Cost Transparency and TBM Studio, including model configuration, data transformation, allocation design, reporting, troubleshooting, and production support.
Working knowledge of Cloudability and cloud cost management concepts, including cloud cost allocation, tagging or metadata alignment, usage analysis, and FinOps reporting support.
Strong understanding of TBM taxonomy, TBM fundamentals, Technology Finance, IT cost allocation, unit costing, showback & chargeback, and product or service cost transparency.
Experience designing or maintaining data flows across financial, operational, infrastructure, application, cloud, and consumer datasets using connectors, file-based loads, APIs, transformations, and validation controls.
Ability to evaluate model design options, identify data or logic issues, recommend durable solutions, and explain impacts to both technical and non-technical stakeholders.
Demonstrated experience operating in a control-oriented environment with strong documentation, reconciliation, lineage, change management, and audit-readiness practices.
Proficiency with Excel and analytical tools for reconciliation, variance analysis, validation, and financial or operational data analysis; SQL, Power Query, or similar data transformation experience preferred.
Lead-level communication, problem-solving, and stakeholder management skills with the ability to influence across Technology, Finance, FinOps, and matrixed partner teams.

Responsibilities

Lead configuration, sustainment, and enhancement of Technology Business Management and financial modeling capabilities, including data ingestion, transformation, allocation logic, costing methods, and reporting structures.
Apply TBM taxonomy and foundational TBM concepts to support technology cost modeling, product and service costing, showback & chargeback, and executive-level cost transparency outcomes.
Support cloud financial reporting, cloud cost allocation, reporting validation, and FinOps-related analysis in partnership with Technology Finance, FinOps, and technology stakeholders.
Lead data quality, reconciliation, variance analysis, and control activities to ensure financial models and reporting outputs are accurate, explainable, repeatable, and audit ready.
Translate business and finance requirements into scalable model, reporting, data integration, and process solutions; provide guidance to analysts and stakeholders on design tradeoffs and downstream impacts.
Manage outbound financial and operational data exchanges, including extracts, APIs, published datasets, and downstream validation for reporting, forecast, subledger, and allocation consumers.
Develop and maintain runbooks, control documentation, and repeatable operating procedures for monthly refresh cycles, production support, change management, and issue resolution.
Identify automation, AI, and process improvement opportunities that improve data reliability, cycle time, stakeholder self-service, and overall Technology Finance operating effectiveness.