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# Data Quality Engineer

- **Company:** Synergetic
- **Location:** London, UK (Remote available)
- **Contract:** Temporary contract
- **Skills:** Airflow, Amazon Web Services, Amazon S3, Cloud Database, Data Validation, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Profiling, Python (Programming Language), Meta-Data Management, Standard Sql, SQL Databases, Technical Data Management Systems, Cloud Platform System, SC Clearance, Data Lineage, Data Pipelines
- **Published:** November 4, 2026
- **Apply:** https://www.careerboard.com/gb/en/find-jobs-in-United-Kingdom/-EACE575BF9701A07B4/

## About the Role

Clearance: YOU MUST HOLD ACTIVE SC CLEARANCE USED WITHIN LAST 12 MONTHS

This is a specialist Data Quality role, not a general Data Engineering position. Applicants must have demonstrable hands-on Data Quality experience.

We are seeking an experienced Data Quality Engineer with strong Python and SQL skills and proven experience implementing Data Quality frameworks, rules and automated controls within cloud-based data environments., * Strong hands-on Python and SQL.

 * Proven experience in Data Quality profiling, rule definition, validation and automated controls.
 * Experience with Critical Data Elements, DQ metrics, tolerances and issue management.
 * Ability to translate business Data Quality requirements into technical solutions.
 * Experience working with cloud-based data platforms and ETL/ELT pipelines.

 * dbt and Apache Airflow.
 * AWS (S3, Athena, Glue).
 * Data Quality dashboards and reporting.
 * Data governance, metadata management and data lineage.

## Description

* Identify and prioritise Critical Data Elements (CDEs), including associated data models and quality metrics.
 * Perform data profiling to identify anomalies, inconsistencies and quality issues.
 * Translate business requirements into technical Data Quality rules and validation checks.
 * Define and configure DQ thresholds, tolerances and automated controls.
 * Develop Python and SQL-based Data Quality checks across data pipelines.
 * Support dbt transformations, testing and documentation, alongside Airflow scheduling and orchestration.
 * Develop DQ monitoring, dashboards and reporting capabilities.
 * Investigate Data Quality issues, identify root causes and support remediation.