Data Quality Engineer

• 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.

• 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.

Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team.