About the job
Achieving our goals starts with supporting yours. Grow your career, access top-tier health and wellness benefits, build lasting connections with your team and our customers, and travel the world using our extensive route network.
Come join us to create what's next. Let's define tomorrow, together.
Description This role supports the Finance Data Capability team at the intersection of Finance, Data, and AI, assisting in transforming finance data into a scalable, insight-driven asset. It contributes to building standardized data structures, faster insights, and improved decision-making. The role primarily assists senior analysts in executing data, analytics, and AI initiatives, while supporting adoption of GenAI, automation, and modern data platforms. It helps enable an AI-driven finance ecosystem through structured execution and continuous improvement.
Job Overview and Responsibilities
- Support Enterprise-Wide Data Standards, Analytics, Insights & AI
Assist senior analysts on data process improvement and standardization Work with finance datasets for preparation and validation Support GenAI solutions (copilots, automation scripts) Build dashboards with AI-driven insight Support data quality and AI-based anomaly detection Document data logic for scalable AI usage Maintain and validate finance data mappings, while exploring AI-assisted reconciliation, mapping and automation Support finance data governance by defining data quality rules and partnering on controls and lineage, and exception handling
- Support financial transformation initiatives
Support project execution and tracking Support delivery of transformation initiatives (milestones, timelines, reporting), while piloting AI-enabled process automation Identify AI and automation opportunities Contribute to business cases by quantifying impact from AI, automation, and improved data architecture This position is offered on local terms and conditions. Expatriate assignments and sponsorship for employment visas, even on a time-limited visa status, will not be awarded. This position is for United Airlines Business Services Pvt. Ltd - a wholly owned subsidiary of United Airlines Inc.
Qualifications
What's needed to succeed (Minimum Qualifications)
Bachelor's degree Information Systems, Computer Science, Data Science, Finance, Accounting, or related field 2+ years of finance and/or technology
Experience
Experience
in Finance Analytics, Data Analytics, Finance Systems, or Technology Transformation Strong expertise in finance data, reporting, analytics, data modeling, and master data governance
Experience
working with large, complex datasets using platforms such as Databricks, Snowflake, Foundry, SQL Server, or similar Hands-on experience with Power BI and/or Tableau for reporting and visualization Must have working knowledge of Oracle EDM, complimentary would be Oracle ERP, Oracle EPM Strong proficiency in SQL and data analysis.
Experience
using Python for analytics, automation, or process improvement
Experience
with Alteryx or similar ETL/data transformation tools Familiarity with GenAI tools (e.g., Copilot, ChatGPT), AI-assisted analytics, and agentic workflows/intelligent automation Excellent verbal and written communication skills, with the ability to present complex information to both finance and non-finance stakeholders A highly motivated individual with a strong understanding of how people, processes, data, and technology work together to produce a strong and sustainable capability Ability to work independently with minimal guidance needed What will help you propel from the pack (Preferred Qualifications):
Master's degree Masters in Analytics/Data Science
Experience
in airline or similar industry
Experience
with PMO/project management tools such as Smartsheet, JIRA, or similar platforms Understanding of finance data standards, governance, controls, and business processes across domains such as GL, AP, AR, FP&A, and Treasury
Experience
establishing or driving data definitions, business rules, and reporting standards across functions.
Exposure to cloud-based data and analytics architectures (e.g., AWS Data Lake, Azure, or similar platforms)