A Data Engineer is expected to develop monitor and optimize data quality rules and processes to ensure reliable and accurate enterprise data.
They identify and resolve data anomalies through profiling cleansing and validation workflows.
The role ensures that business decisions are driven by complete consistent and high-integrity data across systems.
Key Responsibilities:
Design implement and maintain data quality frameworks rules and processes to ensure the accuracy completeness and reliability of enterprise data.
Collaborate with data governance MDM and data integration teams to enforce consistent data quality standards across systems.
Conduct data profiling anomaly detection and validation to identify quality gaps and root causes.
Develop automated data quality checks and workflows for continuous monitoring and remediation.
Define and manage key data quality metrics (DQ KPIs) and build dashboards to visualize performance trends.
Document data quality rules issue logs and remediation actions as part of the data governance framework.
Work closely with business data stewards and domain owners to define data quality expectations and acceptance thresholds.
Support audits compliance reviews and data management maturity assessments by providing DQ evidence and reports.
Lead the design and deployment of scalable data quality solutions across multiple data domains and systems.
Define enterprise data quality standards policies and rule frameworks aligned with governance strategy.
Establish data quality scorecards thresholds and escalation workflows for high-impact issues.
Conduct root cause analysis and lead remediation programs for chronic data issues.
Mentor junior engineers and promote data quality best practices across teams.
Participate in compliance with the local data management regulations (e.g. NDMO NDI).
Design build and maintain scalable secure and robust data pipelines for efficient data ingestion and transformation with a strong focus on building quality checks into the infrastructure itself.
Collaborate with cross-functional and leadership teams (Data Scientists Analysts and Business Leaders) to translate complex business requirements into technical data quality solutions.
Research evaluate and recommend new data quality tools technologies and practices to enhance the reliability security and scalability of the data infrastructure.
Qualifications :
- Bachelors or Masters degree in Computer Science Information Management or related discipline.
- 5 years of experience in Data Quality Data Governance or Data Engineering roles.
- Proven hands-on experience with enterprise DQ tools (e.g. Informatica DQ/IDMC Ataccama Talend DQ SAS DQ).
- Strong expertise in data profiling rule design standardization and exception management.
- Proficiency in SQL and scripting (Python Shell or Scala) for advanced data validation.
- Deep understanding of data governance frameworks and collaboration with data stewards.
- Ability to design DQ dashboards scorecards and KPIs to monitor performance.
- Strong communication skills for presenting findings and recommendations to business stakeholders.
- Experience mentoring junior engineers or managing small DQ workstreams.
- CDMP Associate Certificate and/or Informatica IDQ Certificate and/or IDMC DQ Certificate
- Experience defining data quality strategy and embedding DQ into enterprise data platforms (MDM DWH Data Lake).
- Understanding of data lineage root cause analysis and impact assessment.
- Knowledge of metadata management and integration with data catalogs.
- Familiarity with regulatory and compliance requirements (e.g. NDMO NDI).
- Ability to automate DQ workflows through orchestration or CI/CD pipelines.
Additional Information :
Business Unit: Data & Intelligence
Level: Mid - Senior Level
Remote Work :
No
Employment Type :
Full-time
A Data Engineer is expected to develop monitor and optimize data quality rules and processes to ensure reliable and accurate enterprise data.They identify and resolve data anomalies through profiling cleansing and validation workflows.The role ensures that business decisions are driven by complete c...
A Data Engineer is expected to develop monitor and optimize data quality rules and processes to ensure reliable and accurate enterprise data.
They identify and resolve data anomalies through profiling cleansing and validation workflows.
The role ensures that business decisions are driven by complete consistent and high-integrity data across systems.
Key Responsibilities:
Design implement and maintain data quality frameworks rules and processes to ensure the accuracy completeness and reliability of enterprise data.
Collaborate with data governance MDM and data integration teams to enforce consistent data quality standards across systems.
Conduct data profiling anomaly detection and validation to identify quality gaps and root causes.
Develop automated data quality checks and workflows for continuous monitoring and remediation.
Define and manage key data quality metrics (DQ KPIs) and build dashboards to visualize performance trends.
Document data quality rules issue logs and remediation actions as part of the data governance framework.
Work closely with business data stewards and domain owners to define data quality expectations and acceptance thresholds.
Support audits compliance reviews and data management maturity assessments by providing DQ evidence and reports.
Lead the design and deployment of scalable data quality solutions across multiple data domains and systems.
Define enterprise data quality standards policies and rule frameworks aligned with governance strategy.
Establish data quality scorecards thresholds and escalation workflows for high-impact issues.
Conduct root cause analysis and lead remediation programs for chronic data issues.
Mentor junior engineers and promote data quality best practices across teams.
Participate in compliance with the local data management regulations (e.g. NDMO NDI).
Design build and maintain scalable secure and robust data pipelines for efficient data ingestion and transformation with a strong focus on building quality checks into the infrastructure itself.
Collaborate with cross-functional and leadership teams (Data Scientists Analysts and Business Leaders) to translate complex business requirements into technical data quality solutions.
Research evaluate and recommend new data quality tools technologies and practices to enhance the reliability security and scalability of the data infrastructure.
Qualifications :
- Bachelors or Masters degree in Computer Science Information Management or related discipline.
- 5 years of experience in Data Quality Data Governance or Data Engineering roles.
- Proven hands-on experience with enterprise DQ tools (e.g. Informatica DQ/IDMC Ataccama Talend DQ SAS DQ).
- Strong expertise in data profiling rule design standardization and exception management.
- Proficiency in SQL and scripting (Python Shell or Scala) for advanced data validation.
- Deep understanding of data governance frameworks and collaboration with data stewards.
- Ability to design DQ dashboards scorecards and KPIs to monitor performance.
- Strong communication skills for presenting findings and recommendations to business stakeholders.
- Experience mentoring junior engineers or managing small DQ workstreams.
- CDMP Associate Certificate and/or Informatica IDQ Certificate and/or IDMC DQ Certificate
- Experience defining data quality strategy and embedding DQ into enterprise data platforms (MDM DWH Data Lake).
- Understanding of data lineage root cause analysis and impact assessment.
- Knowledge of metadata management and integration with data catalogs.
- Familiarity with regulatory and compliance requirements (e.g. NDMO NDI).
- Ability to automate DQ workflows through orchestration or CI/CD pipelines.
Additional Information :
Business Unit: Data & Intelligence
Level: Mid - Senior Level
Remote Work :
No
Employment Type :
Full-time
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