Senior Data Analyst - FP&A Data Quality & Validation

Tech Mirrors; Frisco (TX); On-site; Full time

Job description

Job Title: Sr. Data Analyst
Job Location: Frisco, TX (Onsite)
Job duration: Long Term Contract

We are getting many rejections for Data Analyst position. Please find below the required skill sets and share resumes accordingly. This is top priority.

Required Skill Sets

• SQL – Advanced Analytical & Profiling Queries
• Data Profiling, Validation & Quality Frameworks
• Data Reconciliation Across Sources
• Snowflake & Databricks (Analytical Use)
• Azure Ecosystem (Working Knowledge)
• Oracle Planning Modules (BPM / EDM / EBPCS) Familiarity
• Finance / FP&A Domain Knowledge
• Root Cause Analysis & Critical Thinking
• Stakeholder Communication (Business + Tech Bridge)
• Ownership, Urgency & Documentation Discipline

Required Skills

Mandatory Skills: Finance FP&A / Planning (BPM)(Finance Domain) Data Quality & Validation

Role Overview

We are seeking a Senior Data Analyst to support a critical finance transformation initiative by ensuring data accuracy, completeness, and readiness, especially for the FP&A / Planning (BPM) workstream. The organization is implementing planning modules (Oracle BPM / EDM / EBPCS) and will feed data from upstream platforms such as Snowflake / Databricks into these systems. This role will focus on vetting and validating upstream and downstream data, performing analysis to ensure correctness, and partnering closely with the Data Architect to support the RDMP platform goals. This role requires someone senior who can ask the right questions, challenge assumptions, investigate upstream issues, and ensure data truly solves the business problem (single source of truth for finance).

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.