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Key Responsibilities and Required Skills for a Junction Quality Analyst

💰 $65,000 - $95,000

Data AnalyticsQuality AssuranceITData ManagementSystem Integration

🎯 Role Definition

The Junction Quality Analyst is a specialized and pivotal role focused on safeguarding the integrity, accuracy, and consistency of data as it traverses between different applications, databases, and platforms. This professional acts as the ultimate quality gatekeeper for data integration points—the "junctions"—by designing and implementing comprehensive testing strategies, data validation rules, and robust monitoring frameworks. They meticulously analyze data pipelines, ETL processes, and API-based data exchanges to detect, diagnose, and drive the resolution of quality issues, ensuring that all downstream analytics, reporting, and business operations are built upon a foundation of high-quality, trustworthy data.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst / Data Analyst
  • QA Tester / QA Engineer (with data focus)
  • Business Systems Analyst

Advancement To:

  • Senior Junction Quality Analyst / Senior Data Quality Analyst
  • Data Governance Manager
  • Data Engineer / BI Engineer

Lateral Moves:

  • Data Steward
  • Business Intelligence Analyst
  • Systems Analyst

Core Responsibilities

Primary Functions

  • Develop, implement, and uphold comprehensive data quality standards, policies, and procedures specifically for data integration points and ETL processes.
  • Design, create, and execute detailed, end-to-end test plans, test cases, and test scripts to rigorously validate data transformations and business logic within data pipelines.
  • Perform in-depth root cause analysis on complex data quality issues, collaborating closely with data engineering and development teams to implement lasting and effective solutions.
  • Utilize advanced SQL scripting to profile large datasets, perform complex data validation, and conduct deep-dive analysis across diverse source and target systems.
  • Establish and maintain automated monitoring systems for critical data pipelines and integration junctions, configuring alerts to proactively notify teams of failures or data anomalies.
  • Create and manage a suite of data quality dashboards and performance reports for technical and business stakeholders, clearly visualizing key quality metrics, trends, and issue resolution progress.
  • Work directly with business analysts, product owners, and other stakeholders to thoroughly understand data requirements and define clear, measurable acceptance criteria for data quality.
  • Conduct meticulous source-to-target data mapping validation and data lineage tracing to ensure information is accurately and completely transformed and loaded according to business specifications.
  • Validate the structural and payload integrity of data flowing through APIs, checking for correct formatting, data types, value constraints, and adherence to defined schemas.
  • Identify, document, and track data quality defects, inconsistencies, and gaps using defect management tools (like Jira), managing the full defect lifecycle from discovery through to verification and closure.
  • Collaborate with the data governance team to enforce enterprise-wide data standards and contribute to the maintenance of the enterprise data dictionary and business glossary.
  • Perform thorough regression testing on data pipelines and ETL jobs following system upgrades, schema changes, or code deployments to ensure continued data integrity and prevent regressions.
  • Analyze historical data sets to identify recurring patterns of data degradation and proactively recommend process, system, or architectural improvements to prevent future issues.
  • Develop and maintain a centralized, version-controlled repository of reusable data quality rules that can be automated and applied consistently across various data junctions.
  • Assess the potential impact of proposed system changes on data quality and existing integrations, providing expert feedback and risk mitigation recommendations to project teams.
  • Participate actively in the design and architectural review of new data integration solutions, championing quality, testability, and data integrity from the earliest stages of development.
  • Automate repetitive and manual data validation tasks using scripting languages like Python or by leveraging specialized data quality tools to improve efficiency and test coverage.
  • Ensure all data handling and testing activities are in strict compliance with relevant data privacy and security regulations, such as GDPR, CCPA, and HIPAA.
  • Facilitate complex data reconciliation processes between disparate systems (e.g., finance, CRM, operations) to ensure transactional and master data is perfectly aligned and trustworthy.
  • Lead and contribute to data cleansing and enrichment initiatives by defining the business rules for data correction and overseeing the execution of data improvement projects.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis to investigate potential quality concerns or answer specific business questions.
  • Contribute to the organization's overarching data strategy and technology roadmap by providing insights on quality assurance and testing.
  • Collaborate with various business units to translate their operational and analytical data needs into clear, actionable engineering and testing requirements.
  • Participate in sprint planning, daily stand-ups, retrospectives, and other agile ceremonies as an embedded member of the data engineering or analytics team.

Required Skills & Competencies

Hard Skills (Technical)

  • Advanced SQL: Deep proficiency in writing complex SQL queries, including joins, subqueries, window functions, and CTEs for data validation and profiling across relational and non-relational databases.
  • ETL/ELT Testing: Strong understanding of ETL concepts and hands-on experience testing data pipelines built with tools like Informatica, Talend, SSIS, dbt, or custom scripts.
  • Scripting Languages: Practical knowledge of a scripting language, primarily Python (with libraries like Pandas, Pytest) or R, for data manipulation, analysis, and test automation.
  • Data Visualization: Experience using data visualization tools such as Tableau, Power BI, or Looker to build dashboards and reports that communicate data quality metrics effectively.
  • Database Systems: Hands-on experience with various database technologies, including SQL Server, Oracle, PostgreSQL, and familiarity with cloud data warehouses like Snowflake or BigQuery.
  • API Testing: Understanding of API architecture (REST, SOAP) and experience with tools like Postman or Insomnia to test data exchange endpoints for accuracy and performance.
  • Data Profiling: Ability to use data profiling techniques and tools to discover data characteristics, identify quality issues, and understand data structures.

Soft Skills

  • Meticulous Attention to Detail: A sharp eye for detail and an unwavering commitment to accuracy, capable of spotting subtle inconsistencies that others might miss.
  • Analytical & Critical Thinking: The ability to dissect complex problems, evaluate information from multiple sources, and use logic to diagnose the root cause of data issues.
  • Collaborative Communication: Excellent verbal and written communication skills, with the ability to clearly articulate complex technical findings to non-technical stakeholders and work effectively across teams.
  • Systematic Problem-Solving: A structured and methodical approach to troubleshooting, from initial investigation and hypothesis testing to solution implementation and verification.
  • Inquisitive Mindset: A natural curiosity and a proactive drive to question the data, explore its meaning, and continuously seek opportunities for improvement.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor’s Degree in a quantitative or technical field.

Preferred Education:

  • Master’s Degree or relevant professional certifications (e.g., Certified Data Management Professional - CDMP, ISTQB).

Relevant Fields of Study:

  • Computer Science
  • Information Systems / Management Information Systems
  • Statistics / Mathematics
  • Data Science / Business Analytics

Experience Requirements

Typical Experience Range:

  • 3-5 years of professional experience in a data-focused role such as Data Quality, Data Analysis, QA Engineering, or Business Intelligence.

Preferred:

  • Experience working in data-intensive environments, particularly within industries like finance, healthcare, e-commerce, or insurance.
  • Proven track record of testing and validating large-scale data warehouse and ETL solutions.
  • Direct experience working within an agile development framework and using associated tools (e.g., Jira, Confluence).
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