Payroll Data Analyst: Data Quality & Validation Specialist

Zone IT Solutions; Melbourne; On-site; AUD 90,000 - 130,000; Full time

Job summary

Zone IT Solutions is seeking an experienced Payroll Data Analyst to work with live payroll data across Oracle HCM, Kronos, SAP, JD Edwards and ServiceNow. You will define data reads, profile extracts, establish baselines and validate the prototype against payroll results.

You will obtain and document field definitions, volumes and quality for each source, support data mapping, and ensure accurate reconciliation and data quality throughout the project.

Qualifications

• Experience profiling real payroll and time extracts.
• Strong SQL and data quality experience.
• Familiarity with Oracle HCM Cloud data and HCM Extracts.

Responsibilities

• Obtain and profile real extracts from Oracle HCM, Kronos, SAP and JDE pay results, ServiceNow tickets, and document each source's fields, volumes and quality.
• Agree the classification per data set with Downer Cyber, defining what is tokenised and who may see it.
• Specify the HCM objects, fields, extracts and Atom feeds the platform needs, the drift by attribute and the starter and change field sets per payroll.
• Build the de-identified validation set using real people, periods and their actual pay results, and run comparisons against the prototype's engine with the AI Engineer, classifying every difference.
• Define the target data model, finance feed format and reconciliation the platform must pass.

Skills

SQL

Data profiling

Data quality

Payroll data

Tools

Oracle HCM Cloud

HCM Extracts

REST APIs

Kronos

SAP

JD Edwards

ServiceNow

Job description

Zone IT Solutions is seeking an experienced Payroll Data Analyst to work with live payroll data across Oracle HCM, Kronos, SAP, JD Edwards and ServiceNow. You will define data reads, profile extracts, establish baselines and validate the prototype against payroll results.

You will obtain and document field definitions, volumes and quality for each source, support data mapping, and ensure accurate reconciliation and data quality throughout the project.