Data Engineer

Job Description

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.

• 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.

• Bachelor’s or Master’s 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 work streams.
• 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.

Data & Intelligence

Mid - Senior Level