Remote Data Quality Engineer & Scientist

Vervic Inc.; United States; Remote; USD 120,000 - 180,000; Full time

Benefits offered by this job

401(k)

401(k) matching

Dental insurance

Health insurance

Paid time off

Job summary

Vervic Inc. is seeking a Data Quality Engineer / Data Scientist to establish data quality, profiling, reconciliation, and analytics capabilities across the CCN Next Gen ecosystem.

The role focuses on data accuracy, completeness, traceability, and integrity as data moves between systems and partners. The candidate will apply data engineering, statistics, automation, and AI-assisted techniques to identify anomalies, validate transformations, and expose integration issues, collaborating with

Qualifications

• Bachelor's degree in a data-related field or equivalent professional experience.
• 10+ years in data engineering, data quality, data science, or analytics engineering.

Responsibilities

• Design reusable data quality frameworks for end-to-end system integration.
• Automate data quality controls for accuracy, completeness, and integrity.
• Develop reconciliation methods across source, transformation, and downstream data.
• Profile data to establish baselines and detect anomalies across environments.
• Collaborate with CCN teams to validate data quality and drive improvements.
• Support synthetic data quality, governance, and testing within QA processes.

Skills

SQL

Python

R

Data profiling

Data quality

Data reconciliation

Data modeling

Data lineage

CI/CD

Git

REST APIs

Education

Bachelor's degree in Data Science/CS/Statistics

Tools

Pandas

Spark

Airflow

Job description

Vervic Inc. is seeking a Data Quality Engineer / Data Scientist to establish data quality, profiling, reconciliation, and analytics capabilities across the CCN Next Gen ecosystem.

The role focuses on data accuracy, completeness, traceability, and integrity as data moves between systems and partners. The candidate will apply data engineering, statistics, automation, and AI-assisted techniques to identify anomalies, validate transformations, and expose integration issues, collaborating with