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# Data Warehouse Analyst

- **Company:** IO Datasphere
- **Location:** Madison, WI, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Databases, Data Architecture, Data Cleansing, Data Profiling, Data Warehousing, IBM DB2, Database Design, Python (Programming Language), Metadata, Microsoft SQL Server, Oracle Databases, Oracle SQL Developer, Oracle Warehouse Builder, SQL Databases, Data Management
- **Published:** August 11, 2026
- **Apply:** https://www.dice.com/job-detail/104ed859-375c-41a0-9ac9-02360404e0d1

## About the Role

* Excellent organization, analytical, and communication skills

Location: Madison, WI (WI residency required. Must be WI resident or willing to relocate prior to start date at own expense.)

Contract: 3+ months (This is a limited engagement expected to end 12/31/2026 but could be extended)
<>Skills Required:

· 5 years Metadata discovery and documentation experience

· 5+ years Data Profiling experience

· 3 years Advanced Python experience

· 5 years Data quality assessment and identification experience

· 5 years Creating and reviewing SQL scripting experience

· 5+ years' Experience with database design concepts

· 5 years' Knowledge of Oracle PL/SQL

· 5 years Metadata tools experience

· 5 years' Experience with Oracle 11g/DB2/SQL Server

· 4 years' Experience with Oracle data dictionary views
<> Skills Desired - A plus to have:

 * · Experience with Human Services business intelligence

*** Rate depends on experience

*** Candidates authorized to work in the US are encouraged to apply. Only for W2 candidates only. However, we cannot accept OPT or CPT visas at this time.

## Description

Our client seeks a Data Warehouse Analyst. This position will be responsible for the assessment of data quality and metadata within client's data architecture. They will be responsible for the data profiling, analysis and documentation of data quality.
<>Tasks:

· Audit existing databases to assess data characteristics such as completeness, accuracy, outliers, and duplicates.

· Investigate and define automated validation rules to guarantee data accuracy and catch errors at the point of entry.

· Develop data cleansing and remediation recommendations for data irregularities.

· Establish key performance indicators (KPIs) to measure data health, and build reporting dashboards to track quality metrics and trends.

· Investigate the origins of recurring data errors (e.g., system bugs, user errors, integration issues) and recommend targeted, systemic solutions.

· Document and define key metadata around database architecture.

· Make recommendations on data maintenance and data quality.

· Collaborate with Data Modelers and Data Architects on recommendations for key data management and data quality efforts.


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