Data Engineer

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If making a difference matters to you, then you matter to us.

Join us, at Wolters Kluwer, and be part of a dynamic global technology company that makes a difference every day. We’re innovators with impact. We provide expert software & information solutions that the world’s leading professionals rely on, in the moments that matter most.

You’ll be supported by collaborative colleagues who share a purpose. We are 21,000 people unique in our dreams, life stories, abilities, and passions who come together every day with one ambition: to make a difference.​ We do our best work together, connecting to create new innovations with impact.

A data engineer develops and optimizes Enablon’s conceptual and logical data systems. Enablon has a very rich product ecosystem which is very data centric: 

• Our systems need to share data to support joint scenarios where products interact with each other and share the same data referential (locations, sites, organization, equipment's). 

• We want to develop and maintain data ingestion and processing systems 

• We need to ensure data consistency and accuracy through data validation and cleansing techniques 

To succeed in this role, he/she knows how to examine new data system requirements and implement migration models. He/she has proven experience in data analysis and data management, with excellent analytical and problem-solving abilities.  

He/she works together with others in solving complex technical problems. He/she uses analytical thought to exercise judgement and identify innovative solutions. He/she works independently – with guidance in more complex situations – to impact the achievement of project objectives. Their work is guided by technical and professional standards and guidelines. 

Responsibilities:

• Develop, and maintain efficient ETL processes to extract, transform, and load data from various sources using tools like Snowflake. 

• Assist in the migration of data from legacy systems to new solutions.

• Implement data models to support analytics and reporting requirements. 

• Implement data quality checks and validation processes to ensure data accuracy and completeness. 

• Optimize data processing workflows and queries to improve performance and reduce latency. 

• Work closely with analysts, and other stakeholders to understand data requirements and deliver solutions. 

• Monitor data pipelines and systems for issues and troubleshoot any problems that arise. 

• Ensure data security and compliance with relevant regulations and standards. 

• Ensure all ETL pipelines are running smoothly, with timely delivery of data. 

• Continuously monitor and improve the performance of data processing workflows. 

Requirements:

• Education: Bachelor's degree in Information Technology or related field 

• A minimum of 1 year experience in a similar role 

• Good knowledge of database structure systems  

• Excellent technical and analytical skills 

Other Knowledge, Skills, Abilities or Certifications: 

• Problem solver with a “can do”, positive and pragmatic attitude 

• Good interpersonal relationship 

• Fluent English is necessary 

• Working knowledge of Agile development 

• Programming Languages: Python  

• Can be a plus: C# 

• Software Design: design patterns, event sourcing, algorithms, microservices, web services (SOAP/REST), 

• NoSQL/SQL: database design, development and data modeling (e.g. AzureSQL, PostgreSQL, CosmosDB); 

• Big Data Tools: Snowflake, data lakes (Azure suite) 

• Can be a plus: Spark/DataBricks, Kafka, Azure Data Factory 

• Working knowledge of Microsoft Azure and their data services 

• DevOps & Automation: containers and container orchestration (Docker), infrastructure as code (Terraform & Pulumi), CI/CD pipelines (GitHub Workflows) 

Nice to Have (Will be considered a plus) 

• Experience in data modeling techniques and methodologies. 

• Familiarity with data modeling tools such as Erwin Data Modeler, IBM InfoSphere Data Architect, Oracle SQL Developer Data Modeler or similar. 

• Knowledge of creating and maintaining conceptual, logical, and physical data models. 

• Understanding of data warehousing concepts and design. 

• Experience with metadata management and data governance practices. 

• Ability to translate business requirements into data models that support long-term solutions. 

• Familiarity with data integration and data architecture best practices. 

Travel requirements: occasional travel may be required to regional offices.