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# Data Quality Engineer - Remote

- **Company:** Levi, Ray & Shoup, Inc.
- **Location:** Philadelphia, PA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $62,400.0 - $124,800.0
- **Contract:** Temporary to permanent
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Information Technology Consulting, Databases, Continuous Integration, Data Validation, Data Cleansing, Data Governance, Data Integrity, Extract Transform Load (ETL), Dataspaces, Data Warehousing, Meta-Data Management, Cloud Services, Standard Sql, DataOps, SQL Databases, Simple Object Database Access, Informatica Powercenter, Great Expectations (Foster Youth College-readiness and Support Program), Snowflake, Data Lineage, Collibra, Data Management, Data Pipelines, Databricks
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-quality-engineer-remote-philadelphia-pa--6c364135-a187-47e4-b654-90c5350e170a

## About the Role

Data Quality, Quality Engineering, Consulting, Quality Engineering, Data Quality, Information Technology Consulting, Customer Relations, Ecosystems, Database Extract Transform and Load (ETL), Data Management, * 5+ years of experience in ETL/ELT development and/or data quality engineering

 * Proven experience implementing data quality controls within data pipelines
 * Strong hands-on experience with:
 + SQL (advanced querying, troubleshooting, optimization)
 + ETL orchestration tools (e.g., Airflow, dbt)
 * Experience with enterprise data quality platforms such as:
 + Collibra
 + Informatica
 + Great Expectations
 + Soda (or similar)
 * Practical experience with data observability concepts (freshness, volume, schema, drift)

Preferred Qualifications

 * Experience with cloud data platforms (Azure, AWS, or GCP)
 * Familiarity with modern data stack (Snowflake, Databricks, BigQuery, etc.)
 * Experience building data quality dashboards and monitoring solutions
 * Understanding of data lineage, metadata management, and cataloging tools
 * Exposure to CI/CD practices for data pipelines, * Strong analytical and problem-solving mindset
 * High attention to detail and data integrity
 * Ability to translate business requirements into data quality rules
 * Effective communication with both technical and non-technical stakeholders
 * Ownership mindset with a focus on accountability and continuous improvement

Success Metrics

 * Reduction in pipeline failures due to data issues
 * Increased detection of data anomalies before downstream impact
 * Improved data observability and reporting coverage
 * Strong alignment between data quality rules and business/governance needs
 * Faster root cause identification and resolution times

Candidate must be able to effectively communicate in English (written & verbal)
Candidate must have permanent authorization to work in the USA for any employer

The base range for this contract position is $30 - $60 per hour, depending on experience. Our pay ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hires of this position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.

## Description

We are seeking a Data Quality Engineer with strong ETL development experience to design, implement, and enforce data quality controls across our data ecosystem. This role blends hands-on pipeline development with advanced data quality practices, ensuring data is reliable, observable, and aligned with governance standards.

You will play a critical role in embedding data quality into end-to-end workflows, supporting trusted analytics and decision-making., Data Quality Engineering

 * Design and implement data quality controls within data pipelines, including:
 * Validation rules (accuracy, completeness, consistency)
 * Anomaly detection mechanisms
 * Enforcement patterns such as gating, alerting, and failure handling
 * Define and maintain data quality SLAs and thresholds

ETL/ELT Development & Orchestration

 * Develop and maintain scalable ETL/ELT pipelines using tools such as dbt, Airflow, or similar
 * Integrate data quality checks directly into pipeline workflows to ensure issues are detected early
 * Build reusable frameworks and components for automated data validation

Data Observability & Monitoring

 * Implement and manage data observability practices, including:
 * Freshness monitoring
 * Volume checks
 * Schema validation
 * Data drift detection
 * Surface insights via dashboards and alerting systems to proactively identify issues
 * Partner with engineering teams to improve pipeline reliability and transparency

Data Quality Tools & Platforms

 * Leverage enterprise tools such as Collibra, Informatica, Great Expectations, Soda, or similar to:
 * Define and manage quality rules
 * Monitor data health
 * Track issue resolution workflows
 * Contribute to tool selection, configuration, and optimization

Root Cause Analysis & Issue Resolution

 * Use strong SQL and analytical skills to investigate and resolve data issues
 * Perform root cause analysis across upstream/downstream systems
 * Document findings and implement preventive controls

Data Governance Alignment

 * Align data quality practices with data governance frameworks, policies, and standards
 * Collaborate with data stewards and business owners to define ownership and accountability
 * Ensure data quality rules reflect business definitions and regulatory requirements