Data Quality Engineer

The Company:

Sucafina is the leading sustainable Farm to Roaster coffee company, with a family tradition in commodities that stretches back to 1905. Today, with more than 1,400 employees in 34 countries, we help stakeholders worldwide to find the perfect coffee solutions. We embed technology, innovation, and sustainability throughout the supply chain, creating shared value for all by Investing in Farmers, Caring for People, and Protecting Our Planet. For more information, visit www.sucafina.com.

What are we looking for:

We are looking for entrepreneurs, techies, passionate, eager to learn, humble, with a positive attitude and a high level of integrity People. Flexible and willing to take challenges, work and live in coffee-producing countries, People who want to build expertise and a career in the coffee business and are ready to go the extra mile.

What we offer:

We offer within our pleasant family environment, great opportunities to learn and grow, we offer challenges and exposure to multicultural environments, on-merit base compensation, and free coffee around the clock!

Job Overview

The Data Quality Engineer will ensure the accuracy, consistency, and reliability of data across the Microsoft Fabric Data Warehouse, its integrations, and connected services. The role focuses on testing and validating data pipelines, event-driven processes, and Azure-based integrations such as Logic Apps and Service Bus. The engineer will collaborate closely with data engineers, architects, and business units to maintain high-quality, trustworthy data for analytics and reporting.

Key Responsibilities

1. Design, develop, and execute data quality and validation tests for Microsoft Fabric Data Warehouse objects (tables, views, semantic models).
2. Validate ETL/ELT pipelines, including Fabric Dataflows and integrated source systems, for accuracy and completeness.
3. Test and monitor event-driven data processes, including Azure Service Bus messages, queues, and topics.
4. Validate Azure Logic Apps workflows and integrations that move or transform data between systems.
5. Perform end-to-end testing of data ingestion, transformations, and delivery to downstream consumers or BI systems.
6. Develop and maintain automated data quality checks, reconciliation scripts, and test frameworks.
7. Identify, analyze, and report data quality issues, including root cause analysis and impact assessment.
8. Collaborate with Data Engineers, BI teams, and business units to define data quality rules, metrics, and acceptance criteria.
9. Monitor data quality KPIs, implement controls to prevent defects, and ensure reliability in production pipelines.
10. Document test cases, workflows, data quality rules, and testing outcomes clearly for technical and business stakeholders.
11. Familiarity with unit testing, automated data validation, and integration testing tools such as dbt, Great Expectations, tSQLt, pytest, Azure Logic Apps testing, and Service Bus testing scripts.
12. Perform unit testing and validation of Azure Logic Apps workflows and Service Bus message flows.
13. Develop and maintain automated data quality checks and integration tests using tools such as dbt, Great Expectations, or custom scripts.
14. Validate and test API endpoints and integration workflows using tools such as Postman to ensure data is accurately transmitted between systems.
15. Perform end-to-end testing of event-driven processes and service integrations using Postman and automated scripts.

Required Skills & Qualifications

1. Strong experience in data quality testing and validation for data warehouses or large-scale analytics platforms.
2. Hands-on experience with Microsoft Fabric (Data Warehouse, Lakehouse, Pipelines, Dataflows).
3. Proficiency in SQL and experience validating ETL/ELT pipelines.
4. Experience with event-driven architectures and Azure integrations:

• Azure Service Bus (queues, topics, subscriptions)
• Azure Logic Apps for workflow automation

1. Experience testing data integrations across multiple source systems.
2. Familiarity with data quality concepts: completeness, accuracy, consistency, timeliness, uniqueness.
3. Experience with automation and scripting (Python, PySpark, PowerShell).
4. Understanding of data modeling concepts: star schema, snowflake, fact and dimension tables.
5. Strong analytical, problem-solving, and troubleshooting skills.
6. Excellent documentation and communication skills.
7. Familiarity with unit testing, automated data validation, and integration testing tools such as dbt, Great Expectations, tSQLt, pytest, Postman, Azure Logic Apps testing, and Service Bustesting scripts.

Preferred / Nice to Have:

• Experience with data quality frameworks (e.g., Great Expectations).
• Knowledge of Azure Data Factory, Synapse Analytics, and OneLake.
• Familiarity with CI/CD pipelines for data platforms.
• Understanding of data governance, metadata management, and auditing standards.

Soft skills

• Strong analytical skills and capacity to challenge the financial information received
• High sense of organisation and able to manage multiple tasks with strong attention to detail
• Excellent communication skills with the ability to interact with international stakeholders
• Curious, proactive,  keen to learn and ready for new challenges
• Ability to work independently while also having a team-oriented mindset.

Languages

• Excellent knowledge of English (written and verbal communication skills)
• Knowledge of any other language is a plus (French)