Data Engineer

About Devoteam

Devoteam leverages technology alongside strong human values to create meaningful, positive change globally. With over 25 years of experience and a presence in more than 18 countries across EMEA and beyond, the company focuses on innovation and human-centric approaches to impact individuals and communities positively.

Culture and Values

• Fair and Courageous: Decisions are made with fairness and bravery, fostering growth and recognition.
• Ambition and Results: A spirit of entrepreneurship drives performance, growth, and value creation for clients and the company.
• Learning and Innovating: Curiosity fuels continuous learning and innovation to tackle challenges effectively.
• Caring and Sharing: Emphasis on teamwork, support, and collaboration in a positive work environment.

Core values include Respect, Frankness, and Passion.

Role Overview

The Data Engineer role focuses on designing, developing, and optimizing data quality frameworks and processes to ensure enterprise data is accurate, consistent, and reliable. This position involves identifying data anomalies, conducting profiling and validation, and supporting business decisions with high-integrity data aligned across systems.

Key Responsibilities

• Create and uphold data quality frameworks, rules, and monitoring processes to guarantee reliable enterprise data.
• Coordinate with data governance, MDM, and integration teams to apply consistent data quality standards organization-wide.
• Perform data profiling, anomaly detection, and root cause investigations for quality gaps.
• Implement automated workflows for ongoing data quality monitoring and correction.
• Track and report on key data quality performance indicators using dashboards.
• Maintain documented records of data quality regulations, issue logs, and corrective measures.
• Collaborate with business data stewards to establish clear data quality expectations and limits.
• Support audits and compliance reviews by providing necessary data quality documentation and reports.
• Drive the design and deployment of scalable, multi-domain data quality solutions.
• Define enterprise data quality standards and policies in line with governance strategies.
• Create scorecards, thresholds, and escalation mechanisms for data quality issues.
• Lead investigations into chronic data problems and manage remediation initiatives.
• Mentor junior data engineers and promote best practices in data quality.
• Ensure compliance with local data management regulations such as NDMO and NDI.
• Construct robust, secure data pipelines with embedded quality checks for data ingestion and transformation.
• Collaborate with data scientists, analysts, and business leaders to translate requirements into technical data quality solutions.
• Research and recommend innovative data quality tools and technologies to enhance infrastructure reliability and scalability.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Management, or a related field.
• Minimum five years of experience in data quality, governance, or data engineering.
• Hands-on proficiency with enterprise data quality tools (e.g., Informatica DQ/IDMC, Ataccama, Talend DQ, SAS DQ).
• Strong skills in data profiling, designing quality rules, standardization, and managing exceptions.
• Expertise in SQL and scripting languages such as Python, Shell, or Scala to support advanced data validation.
• Comprehensive knowledge of data governance frameworks and effective engagement with data stewards.
• Ability to develop data quality dashboards, metrics, and scorecards.
• Excellent communication skills to convey findings and recommendations to non-technical stakeholders.
• Experience mentoring or managing junior engineers or small teams.
• Professional certifications like CDMP Associate and/or Informatica IDQ/IDMC Data Quality certificates are advantageous.
• Experience embedding data quality strategies into enterprise platforms like MDM, data warehouses, and data lakes.
• Understanding of data lineage, impact analysis, and root cause investigation techniques.
• Familiarity with metadata management and integration with data catalog systems.
• Knowledge of regulatory and compliance requirements (e.g., NDMO, NDI).
• Capability to automate data quality workflows through orchestration or CI/CD pipelines.

Additional Information

Business Unit: Data & Intelligence

Level: Mid to Senior