US - Program Manager - Data Solutions (AI PMO

AuxoAI Engineering Pvt. Ltd.•Irvine, CA

1d•₹140,000 - ₹170,000

About The Position

About AuxoAI AuxoAI helps enterprises transform how they operate by combining business consulting, data, engineering, and Agentic AI. We are building an AI-native consulting model in which every team member is expected to use AI thoughtfully to improve speed, insight, quality, and client outcomes. What You Will Do · Maintain the integrated delivery plan for data migration, conversion, cleansing, reconciliation, and validation activities. · Coordinate business data owners, source-system teams, data engineers, functional teams, testing teams, and the System Integrator. · Track data objects, conversion cycles, mock loads, entry and exit criteria, defects, reconciliations, and business sign-offs. · Manage dependencies between source extraction, transformation rules, Oracle load processes, downstream validation, and cutover sequencing. · Facilitate data readiness reviews and drive resolution of quality, mapping, ownership, timing, and environment issues. · Prepare concise status reporting on conversion progress, data quality, open defects, reconciliation results, and readiness risks. · Support SIT, UAT, business simulation, cutover rehearsals, production migration, and hypercare validation. · Ensure decisions, assumptions, mapping changes, and unresolved data issues are traceable and assigned to accountable owners. AI-Enabled Delivery Responsibilities · Use AI to summarize mapping documents, identify conflicting transformation rules, and highlight incomplete data ownership decisions. · Generate AI-assisted conversion status narratives, reconciliation summaries, defect themes, and data-quality risk insights. · Apply AI to compare source-to-target specifications, workshop decisions, and test evidence for traceability gaps. · Develop repeatable prompts or workflows that improve the speed and consistency of data PMO activities. Common AI-First Expectations at AuxoAI · Use enterprise AI tools such as ChatGPT Enterprise, Claude Enterprise, Gemini, or equivalent platforms to accelerate delivery and improve decision-making. · Apply AI to automate meeting summaries, action-item tracking, status reporting, executive communications, and document synthesis. · Use AI-assisted analysis to identify delivery risks, cross-team dependencies, emerging bottlenecks, and areas requiring leadership attention. · Continuously identify PMO activities that can be simplified, standardized, or automated through AI and workflow automation. · Validate AI-generated outputs for accuracy, confidentiality, traceability, and business relevance before they are used in program decisions. · Collaborate with consulting, data, engineering, and AI teams to pilot and scale AI-enabled delivery practices across the program. What Success Looks Like · Clear delivery visibility · Early risk identification · Responsible AI adoption · Predictable workstream outcomes

Requirements

4-6 years of experience in project coordination, project management, data delivery, or enterprise transformation.
Understanding of data migration concepts including extraction, cleansing, mapping, conversion, validation, and reconciliation.
Experience coordinating cross-functional teams and tracking milestones, dependencies, risks, issues, and decisions.
Strong Excel, documentation, analytical, and communication skills.
Ability to translate technical data issues into clear business and program impacts.
Comfort using AI tools to analyze documents, summarize findings, and improve reporting.

Nice To Haves

Experience with Oracle Fusion data conversion, FBDI, ADFdi, or related Oracle data-load methods.
Exposure to SQL, ETL/ELT, Snowflake, Informatica, Oracle Integration Cloud, or similar platforms.
Experience supporting mock conversions, reconciliations, SIT, UAT, or cutover.
Experience with Jira, Azure DevOps, Power BI, Tableau, Smartsheet, or Microsoft Project.
Familiarity with data governance, data quality, and master data management.

Responsibilities

Maintain the integrated delivery plan for data migration, conversion, cleansing, reconciliation, and validation activities.
Coordinate business data owners, source-system teams, data engineers, functional teams, testing teams, and the System Integrator.
Track data objects, conversion cycles, mock loads, entry and exit criteria, defects, reconciliations, and business sign-offs.
Manage dependencies between source extraction, transformation rules, Oracle load processes, downstream validation, and cutover sequencing.
Facilitate data readiness reviews and drive resolution of quality, mapping, ownership, timing, and environment issues.
Prepare concise status reporting on conversion progress, data quality, open defects, reconciliation results, and readiness risks.
Support SIT, UAT, business simulation, cutover rehearsals, production migration, and hypercare validation.
Ensure decisions, assumptions, mapping changes, and unresolved data issues are traceable and assigned to accountable owners.
Use AI to summarize mapping documents, identify conflicting transformation rules, and highlight incomplete data ownership decisions.
Generate AI-assisted conversion status narratives, reconciliation summaries, defect themes, and data-quality risk insights.
Apply AI to compare source-to-target specifications, workshop decisions, and test evidence for traceability gaps.
Develop repeatable prompts or workflows that improve the speed and consistency of data PMO activities.
Use enterprise AI tools such as ChatGPT Enterprise, Claude Enterprise, Gemini, or equivalent platforms to accelerate delivery and improve decision-making.
Apply AI to automate meeting summaries, action-item tracking, status reporting, executive communications, and document synthesis.
Use AI-assisted analysis to identify delivery risks, cross-team dependencies, emerging bottlenecks, and areas requiring leadership attention.
Continuously identify PMO activities that can be simplified, standardized, or automated through AI and workflow automation.
Validate AI-generated outputs for accuracy, confidentiality, traceability, and business relevance before they are used in program decisions.
Collaborate with consulting, data, engineering, and AI teams to pilot and scale AI-enabled delivery practices across the program.