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# Data Quality Engineer

- **Company:** Ampcus Inc
- **Location:** Milwaukee, WI, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Automation of Tests, Microsoft Azure, Big Data, Cloud Computing, Data Validation, Data Cleansing, Information Engineering, Extract Transform Load (ETL), Data Masking, Data Profiling, Data Visualization, Apache Hadoop, Monitoring of Systems, Power BI, SQL Databases, Tableau (Software), Data Processing, Scripting, Great Expectations (Foster Youth College-readiness and Support Program), Snowflake, Apache Spark, Information Technology, Apache Kafka, Databricks
- **Published:** October 8, 2026
- **Apply:** https://www.careerjet.com/job/usca7b3db026db416f36652045d26579b7/eaa

## About the Role

* Languages: Expert-level SQL for complex querying and Python for automation scripting.
 * Data Quality Tools: Proficiency in frameworks like dbt, Great Expectations, Soda, Monte Carlo, or Informatica Data Quality, Databricks, Snowflakes.
 * Data Visualization: Proficiency in Power BI, Tableau.
 * Cloud Platforms: Hands-on experience with modern data stacks like Snowflake, AWS (Redshift/S3), Azure, or Databricks.
 * Big Data Tech: Knowledge of Spark, Hadoop, Kafka, or Airflow for managing and monitoring large-scale pipelines., * Experience: Typically, 3-8 years in Data Engineering, ETL Testing, or a specialized Data Quality role.
 * Education: A degree in Computer Science, Data Science, or a related technical field.
 * Soft Skills: High attention to detail, strong communication for explaining technical issues to non-technical stakeholders, and critical thinking for spotting hidden patterns.

## Description

* Data Profiling & Assessment: Analyzing data sources to understand structure, content, and quality; identifying anomalies, duplicates, or missing values.
 * Automated Testing: Designing and implementing automated data validation frameworks (e.g., dbt tests, Great Expectations) within ETL pipelines.
 * Framework Development: Building reusable data quality rules and monitoring systems to detect issues in real-time.
 * Root Cause Analysis: Investigating why data discrepancies occur and working with engineering teams to drive permanent resolutions.
 * Stakeholder Liaison: Bridging the gap between business requirements (what "good data" looks like for a product) and technical implementation.
 * Governance & Compliance: Ensuring data handling meets regulatory standards like GDPR or HIPAA, including implementing data masking and lineage.