Data & Analytics Product Owner (Finance)

Join TK Elevator as a Data & Analytics Product Owner in Atlanta, GA, and drive impactful enterprise data solutions today!EDUCATION & EXPERIENCE:

• Bachelor's degree in Computer Science, Information Science, or a 4-year degree in a related field with a focus in Information Technology
• Proven experience as a Product Owner working closely with Data Engineering teams to build enterprise-scale data solutions.
• Strong business analysis and strategy skills, including building business cases for data and AI/ML investments.
• Understanding of Salesforce data model and experience working with Salesforce data in enterprise analytics is preferred. .
• Hands-on experience with Azure DevOps, Power BI, and SQL for data-driven solutioning.
• Expertise in Enterprise Data Warehousing (EDW), data lake architectures, and ETL/ELT pipeline development.
• Strong understanding of AI/ML use cases and their integration into business processes.
• Excellent stakeholder management skills, with the ability to translate business needs into technical solutions.
• Experience working in an Agile/Scrum environment, supporting sprint planning, backlog grooming, and iterative product delivery.
• Experience in building a connected data ecosystem, integrating ERP, CRM (Salesforce), financial, and operational data sources., preferred
• Knowledge of AI/ML-driven decision-making and automation use cases, preferred
• Background in sales, financial, and field operations analytics, preferred
• Track record of delivering scalable data products that drive business transformation, preferred

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TK Elevator is currently seeking an experienced Data & Analytics Product Owner located in Atlanta, GA to drive the development of enterprise-scale data solutions that empower business stakeholders with actionable insights. This role is critical in enabling a connected data ecosystem by aligning business strategy with data engineering, analytics, and AI/ML initiatives.

As a strategic partner to business and data engineering teams, you will define and prioritize data products, oversee the development of enterprise data warehouses (EDW), and identify AI/ML use cases that deliver measurable business value. You will also play a key role in building business cases for data investments, ensuring alignment with corporate objectives. A strong understanding of the Salesforce data model and its integration with enterprise systems is highly preferred.

ESSENTIAL JOB FUCTIONS:

• Data Enablement & Strategy Development: Partner with business leaders to identify high-impact data opportunities, driving the development of scalable data products.
• Enterprise Data Warehouse & Connected Ecosystem Development: Work closely with data engineering teams to build and enhance a unified, cross-functional data platform that integrates ERP, CRM (Salesforce), finance, and operations data.
• Business Case Development: Build data-driven business cases for new initiatives, quantifying ROI and strategic benefits to secure stakeholder buy-in and funding.
• AI/ML Use Case Identification: Collaborate with business teams to identify AI/ML opportunities, ensuring that data products support predictive analytics and automation.
• Product Ownership for Data Engineering: Define and manage the roadmap for enterprise data infrastructure, including data pipelines, metadata management, and data quality improvements.
• Salesforce Data Model Understanding: Work with CRM, sales, and operations teams to leverage Salesforce data for analytics, forecasting, and business insights, ensuring seamless integration with the enterprise data warehouse.
• Agile Delivery & Backlog Management: Own the product backlog, ensuring clear prioritization of ETL pipelines, reporting enhancements, and data governance initiatives.
• Wireframing & Prototyping: Develop wireframes, data flow diagrams, and conceptual models to guide data architecture and solution design.
• Stakeholder Engagement: Act as the bridge between business and engineering, refining use cases and ensuring timely product delivery.
• Data Governance & Compliance: Ensure adherence to data governance, MDM best practices, and security standards, promoting data democratization and integrity.
• Analytics & AI Enablement: Support AI/ML initiatives by ensuring high-quality data availability and integrating insights into business workflows.