Key Responsibilities and Required Skills for a Data Hygiene Analyst

🎯 Role Definition

The Data Hygiene Analyst is a pivotal role within our data ecosystem, serving as the primary guardian of our organization's most critical asset: its data. This position is fundamentally concerned with ensuring the accuracy, consistency, completeness, and reliability of data across all business systems and platforms. The analyst proactively identifies, investigates, and resolves data quality issues, and is instrumental in developing and enforcing the standards, policies, and procedures that constitute our data governance framework. By maintaining a state of high-quality, trustworthy data, the Data Hygiene Analyst enables confident strategic planning, accurate reporting, and effective operational execution throughout the business.

📈 Career Progression

Typical Career Path

Entry Point From:

Junior Data Analyst or Business Analyst
Data Steward or Data Entry Specialist with an analytical focus
IT Support Analyst with database experience

Advancement To:

Senior Data Hygiene / Data Quality Analyst
Data Governance Manager
Master Data Management (MDM) Lead or Specialist

Lateral Moves:

Data Analyst (focus on business insights)
Business Intelligence Developer
Data Quality Engineer

Core Responsibilities

Primary Functions

Develop and implement comprehensive data quality audits to systematically assess the health of key data assets across various enterprise systems.
Conduct in-depth root cause analysis for identified data quality issues, tracing problems back to their source processes or system entry points.
Design, document, and execute complex data cleansing and data scrubbing routines to remediate identified inaccuracies, duplications, and inconsistencies.
Establish and maintain a master data management (MDM) framework, including the profiling of data and the identification of master data candidates.
Create and manage a comprehensive data quality issue log, meticulously tracking the lifecycle of each issue from discovery to resolution and validation.
Define, measure, and monitor key data quality metrics and Key Performance Indicators (KPIs) such as completeness, validity, accuracy, consistency, and timeliness.
Collaborate with Data Stewards and Data Owners in various business departments to establish and enforce clear data quality rules and standards.
Develop and maintain detailed documentation for data standards, data definitions, and data lineage within the corporate data dictionary or catalog.
Perform ongoing monitoring of data flows and ETL processes to proactively detect potential data integrity breaches or anomalies before they impact downstream systems.
Design and build interactive data quality dashboards and reports for business stakeholders, providing clear visibility into the health of their respective data domains.
Facilitate data de-duplication and record-merging processes, applying business rules to ensure the creation of a "golden record" for key entities like customers and products.
Analyze the impact of poor data quality on business processes and outcomes, quantifying the risks and costs associated with data inaccuracies.
Act as a subject matter expert on data quality tools and techniques, providing guidance and support to other teams and data users.
Participate in the evaluation and implementation of new data quality and data governance software solutions to enhance the organization's capabilities.
Review proposed system changes and new projects to assess their potential impact on data quality and recommend necessary safeguards.
Develop training materials and conduct workshops for end-users on data entry best practices and the importance of maintaining data quality.
Profile source system data to uncover and understand data anomalies, hidden relationships, and quality issues that need to be addressed.
Standardize, enrich, and validate datasets using both automated tools and manual review to improve their overall utility for analytics and operations.
Work closely with the IT and data engineering teams to embed data quality checks and validation rules directly into data ingestion and transformation pipelines.
Manage and resolve data-related inquiries and tickets from business users, providing timely and accurate support.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis to assist business units with specific, time-sensitive inquiries.
Contribute to the organization's overarching data strategy and governance roadmap by providing insights from on-the-ground data quality efforts.
Collaborate with business units to translate their data needs and quality expectations into technical and engineering requirements.
Participate in sprint planning, daily stand-ups, and other agile ceremonies as part of the broader data and analytics team.
Assist in the user acceptance testing (UAT) of new reports and data applications to ensure data accuracy before deployment.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: Proficiency in writing complex queries, including joins, subqueries, and window functions, to profile and manipulate large datasets.
Data Quality Tools: Hands-on experience with enterprise data quality software such as Informatica DQ, Talend Data Quality, or Ataccama.
Data Profiling & Analysis: Strong ability to analyze datasets to identify patterns, anomalies, and inconsistencies.
BI & Visualization Tools: Experience creating reports and dashboards in tools like Tableau, Power BI, or Qlik to communicate data quality metrics.
Advanced Excel: Mastery of Excel for data manipulation, including pivot tables, VLOOKUP/XLOOKUP, and Power Query.
MDM Concepts: Solid understanding of Master Data Management principles, including data modeling, stewardship, and golden record creation.
ETL/ELT Knowledge: Familiarity with the concepts of data extraction, transformation, and loading processes and how they impact data quality.
Scripting Languages: Basic to intermediate proficiency in Python (with libraries like Pandas) or R for data cleansing and analysis is highly desirable.
Data Governance Frameworks: Knowledge of industry standards and best practices, such as those outlined in DAMA-DMBOK.
Database Systems: A strong understanding of relational and non-relational database structures and concepts.

Soft Skills

Meticulous Attention to Detail: An exceptional eye for spotting errors and inconsistencies that others might overlook.
Analytical & Problem-Solving Mindset: The ability to systematically investigate complex problems, identify root causes, and recommend effective solutions.
Strong Communication Skills: Capable of clearly explaining technical data issues and their business impact to non-technical stakeholders.
Collaborative Spirit: A natural team player who can work effectively with diverse groups, from IT engineers to business executives.
Persistence & Tenacity: The drive to follow through on complex data issues until they are fully resolved, even when faced with obstacles.
Methodical & Organized: A structured approach to managing tasks, tracking issues, and documenting processes.
Critical Thinking: The ability to question assumptions, evaluate information objectively, and make sound judgments about data integrity.

Education & Experience

Educational Background

Minimum Education:

Bachelor's Degree from an accredited college or university.

Preferred Education:

Master’s Degree in a relevant field or a professional certification such as Certified Data Management Professional (CDMP).

Relevant Fields of Study:

Computer Science
Information Systems or Management Information Systems (MIS)
Statistics or Mathematics
Business Analytics or Data Science

Experience Requirements

Typical Experience Range:

2-5 years of professional experience in a role focused on data analysis, data management, or data quality.

Preferred:

Direct experience in a dedicated Data Quality, Data Governance, or MDM-focused role.
Proven track record of successfully executing data cleansing, enrichment, and remediation projects.
Experience working within a structured data governance program and collaborating with data stewards.