Everience is an international consulting group delivering AI-augmented digital services and placing people at the heart of the AI revolution.

With a presence in Europe, Africa, Asia and America, Everience offers its 4,000-strong workforce the most demanding and stimulating environment in which to transform and develop their skills, learning about new AI-based roles and building their future employability.

Through its Symbiotic Academy the group offers a unique hub for training, practical application and exchange where everyone can experiment, learn, and progress in the fields of artificial intelligence and data.

In accordance with its core purpose of orchestrating the symbiotic relationship between humans and AI in the workplace, Everience is making the augmented employee the driving force of a “symbiotic age”, where AI enhances talents and opens up new career opportunities.

As a Senior Data Engineer, you will play a key role in managing, integrating, and optimizing data across Upstream and Downstream activities, with a particular focus on data used for forecasting purposes.

You will be responsible for ensuring data quality, consistency, and accessibility across multiple sources, enabling efficient use by business stakeholders, Data Analysts, and Data Scientists. You will contribute to data governance initiatives, support architectural decisions, and implement best practices in data management.

Data Engineering & Data Management:

• Capture, integrate, and process structured and unstructured data from various internal and external sources.
• Design, develop, and maintain robust ETL/ELT pipelines.
• Structure, standardize, qualify, and document data to ensure consistency and usability.
• Map and maintain data lineage and data source documentation.
• Monitor and improve data quality through validation, testing, and cleansing processes.
• Identify and eliminate duplicate, incomplete, or inconsistent data.
• Ensure the quality and reliability of data entering the Data Lake.
• Contribute to the creation and maintenance of data repositories when required.
• Promote and implement best practices in data governance and data management.

Development & Architecture:

• Develop solutions within the enterprise data framework.
• Write technical specifications and design documentation.
• Develop and execute unit tests to ensure solution reliability.
• Participate in technical and architectural discussions and contribute to strategic data platform decisions.
• Support the continuous improvement of data architecture and engineering practices.
• Ensure timely delivery of development activities while maintaining high-quality standards.

Operations & Support:

• Monitor production systems and data pipelines.
• Perform maintenance, troubleshooting, and issue resolution.
• Ensure operational stability and performance of data processing environments.
• Collaborate closely with business stakeholders, architects, developers, Data Analysts, and Data Scientists.

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field.
• Strong experience in Python and .NET/C# development for data processing, scripting, and ETL implementation.
• Advanced knowledge of SQL, relational databases, and data warehouse concepts.
• Experience designing and optimizing data models and database performance.
• Strong understanding of software development best practices.
• Proficiency with version control systems, particularly Git.
• Experience developing and maintaining ETL/ELT solutions.

Preferred

• Hands-on experience with Databricks, including:
  • Development of ETL pipelines
  • Spark cluster management
  • SQL optimization and performance tuning
• Experience with Apache Airflow and the development of DAGs for workflow orchestration.
• Knowledge of Big Data ecosystems, Data Lake architectures, and IoT platforms.
• Familiarity with cloud-based data platforms and modern data engineering frameworks.

All our positions are open to both women and men and are, of course, open to people with disabilities.