Description

Key Responsibilities 

Perform data validation and vetting for FP&A/Planning use cases: 

• Validate upstream datasets feeding Oracle planning modules (BPM/EDM/EBPCS) 
• Identify mismatches, quality gaps, reconciliation issues, and root causes 

Conduct data analysis to ensure accuracy and consistency across domains: 

• Compare finance outputs across systems to prevent multiple answers to key metrics (e.g., revenue) 

Build and execute data quality checks and validation routines: 

• Completeness, accuracy, timeliness, and business rule conformance 

Work closely with the Data Architect to support RDMP platform build: 

• Provide insights on source data issues and domain definitions 
• Support lineage/quality requirements and documentation 

Collaborate with FP&A partners and domain stakeholders: 

• Translate business expectations into validation rules and test cases 
• Ensure the data being delivered aligns to real business outcomes 
• Investigate upstream issues and coordinate with data engineering teams to fix defects quickly.  
• Maintain documentation on data definitions, validation steps, findings, and remediation status. 

Required Qualifications 

• 7–10+ years of experience as a Data Analyst / Senior Data Analyst in enterprise data environments. 
• Strong experience in data analysis and validation using: 
• SQL and analytical techniques 
• Data profiling and reconciliation across multiple sources 

Strong hands-on exposure to: 

• Snowflake 
• Databricks 
• Azure ecosystem (data services understanding) 
• Familiarity with Oracle planning modules (BPM / EDM / EBPCS) and/or finance planning data flows. 
• Strong critical thinking and ability to ask probing questions across upstream/downstream systems. 

Preferred Qualifications 

• Finance domain experience (FP&A, Accounting, Revenue, Tax, Treasury) or similar data-intensive domains. 
• Experience supporting data for forecasting / modeling / ML initiatives (data readiness and quality). 
• Familiarity with data lineage, observability, and governance practices. 

Key Skills & Competencies 

• Strong attention to detail; data accuracy-first mindset. 
• Ability to independently investigate issues and drive closure. 
• Strong ownership and urgency—bias for action. 
• Strong stakeholder communication; can bridge business + technical teams. 

Interview Evaluation Focus 

• Scenario-based data validation and reconciliation discussions 
• Demonstrated examples of finding root causes across complex data flows 
• Ability to challenge upstream issues and define “what good data looks like” 
• Practical SQL/data reasoning skills

Key Skills

Education

Bachelor's degree