Περιγραφή εργασίας

Data Quality Engineer

A.A. Artificient LTD is seeking a Data Quality Engineer to join our growing data and technology team, supporting large-scale data transformation, data migration, analytics, and modernization initiatives.

In this role, you will be responsible for assessing, validating, monitoring, and improving data quality across complex systems and business domains. You will work closely with data architects, data engineers, ETL developers, business analysts, testing teams, and business stakeholders to ensure that data is accurate, complete, consistent, reliable, and fit for purpose.

This is an excellent opportunity for a detail-oriented and analytical professional who enjoys working with large datasets, solving complex data issues, and helping organizations build trust in their data.

Key Responsibilities

• Work with business and technical stakeholders to understand data requirements, business rules, process flows, quality expectations, and key data risks across relevant systems and domains.
• Review data sources, structures, flows, and usage patterns to understand where data is created, transformed, validated, consumed, and reported.
• Profile and assess datasets to identify missing values, duplicates, inconsistencies, unusual patterns, invalid values, reconciliation gaps, and other potential data quality issues.
• Translate business, technical, and operational requirements into practical data quality checks, validation rules, and control logic using SQL, Python, and relevant data quality tools.
• Validate data relationships, source-to-target mappings, transformation logic, reference data, lookup values, record counts, totals, and key attributes across source, staging, transformation, and target environments.
• Support data cleansing, standardization, enrichment, deduplication, and remediation activities by identifying affected records, validating correction logic, and confirming that data improvement actions have achieved the expected outcome.
• Monitor data pipelines and processing outputs to ensure exceptions, rejected records, threshold breaches, regression issues, and processing errors are detected, captured, routed, and investigated appropriately.
• Analyse data quality defects to determine severity, root cause, business impact, ownership, and remediation options, supporting defect triage, issue prioritization, testing, user acceptance, migration readiness, and go-live activities.
• Document validation rules, assumptions, test results, issue investigations, control coverage, lineage insights, lessons learned, and prepare clear dashboards, metrics, reports, and stakeholder updates on data quality status, trends, findings, and improvement opportunities.

Required Experience and Skills

Experience and Education

• 2–7 years of hands-on experience in data quality engineering, data analysis, data testing, or a similar data-focused role.
• Experience handling large datasets, preferably involving millions of records.
• Bachelor’s degree in computer science, Information Systems, Data Science, Engineering, Mathematics, or a related field is preferred.

Technical Skills

• Advanced SQL skills for data profiling, reconciliation, source-to-target validation, duplicate detection, exception reporting, and complex data quality checks.
• Strong ability to validate data across multiple databases, including joins, aggregations, record counts, referential integrity, transformation outputs, and business rules.
• Hands-on experience with Databricks, including notebooks, workflows, Delta tables, lakehouse layers, and large-scale data validation.
• Working knowledge of Python for data analysis and automation, using libraries such as pandas, SQLAlchemy, or similar.
• Experience with data quality tools or frameworks.
• Familiarity with cloud data platforms, preferably Azure, AWS, or Google Cloud, as well as ETL/ELT processes and orchestration tools.

Methodologies, Processes and Soft Skills

• Comfortable working in Agile project environments and contributing across analysis, build, testing, release, and post-implementation support activities.
• Understands the importance of data ownership, governance, privacy, security, and responsible handling of business-critical information.
• Able to investigate complex issues methodically, connect findings across systems and processes, and recommend practical next steps.
• Brings a quality-first mindset, with strong attention to detail and the discipline to challenge assumptions, validate results, and follow issues through to resolution.
• Communicates clearly in English and can translate technical findings into simple, business-friendly language for different audiences.
• Works well with limited supervision, adapts quickly to changing priorities, and remains effective in dynamic project environments.
• Builds productive relationships with business users, data teams, developers, testers, architects, and project stakeholders.
• Organized and reliable, with the ability to manage multiple workstreams, protect confidential information, and contribute to ongoing process improvement. Good understanding of the software development lifecycle and Agile delivery methods.

Nice to Have

Domain Knowledge

• Master’s degree in Computer Science, Data Science, Artificial Intelligence, Information Systems, Engineering, Business Analytics, or a related field.
• Exposure to full-stack development, including front-end, back-end, APIs, databases, or web application development.
• Relevant certifications in AWS, Snowflake, Databricks, data engineering, cloud technologies, or related platforms.

What We Offer

At A.A. Artificient LTD, we offer:

• The opportunity to work on complex, high-impact data transformation and modernization projects.
• Exposure to large-scale data platforms, cloud technologies, and modern data quality practices.
• Collaboration with experienced professionals across data engineering, analytics, business, technology, and governance.
• A dynamic and supportive working environment that encourages learning and continuous improvement.
• Opportunities for professional development, training, and certification.
• Competitive compensation and benefits package.
• Flexible working arrangements, depending on business needs and location.

Equal Opportunity Statement

A.A. Artificient LTD is committed to creating an inclusive and respectful workplace where all individuals are valued. We welcome applications from candidates of all backgrounds, experiences, and perspectives.

 

How to Apply

Send us your CV at admin@artificient.org and join A.A. Artificient LTD in our mission to turn complex data into trusted, reliable, and meaningful business insight.

Bring your SQL skills, your analytical mindset, and your attention to detail — and help us ensure that every dataset is reliable.

 

 

Πώς να υποβάλετε αίτηση

Οι ενδιαφερόμενοι υποψήφιοι παρακαλούνται να στείλουν το βιογραφικό τους στον Διευθυντή Ανθρώπινου Δυναμικού στο admin@artificient.org.

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A.A ARTIFICIENT LIMITED

Artificient reinvents the way IT advisory services need to be provided in the new data era. We have established a thorough flow of ad-hoc processes that merged together to create a data journey for any given Client Business Problem. We begin from the heart and soul of the client by introducing multiple data engineering-related processes in an effort to integrate/migrate, cleanse and structure as well as establish governance and quality to any data the client has. This is a vital step into analyzing and understanding the requirements and thereafter the solution to the problem. We then perform the next step which surfaces the analysis and the assessment conducted into a more business intelligence reporting approach. This will provide a clear and transparent understanding from the client's perspective of what kind of data and information they have and how they can essentially be used. The next pillar of our Data-driven approach is to give the necessary data solutions to the client in order to be able to perform descriptive and advanced analytics. Either by implementing custom solutions or models that assist the client in reaching this vital crucial step. As a final step, we also want the client to become independent of any future worries and towards that, we provide machine learning and AI models which puts the client into the driving position to predict and optimize the way decisions are made and become based on clean data arriving from a single-point-of-truth governed and qualitative.