Data Architecture & Pipelines: Design, develop, and maintain scalable data pipelines and ETL processes to ingest, transform, and deliver structured and unstructured data.

Data Governance & Quality: Implement governance frameworks, data catalogs, and quality assurance processes to ensure compliance, integrity, and security of all data assets.

Data Integration & Storage: Manage data lakes, warehouses, and streaming platforms; optimize storage solutions for performance and cost-efficiency.

Enable AI & Analytics: Prepare and curate datasets for machine learning and advanced analytics projects, ensuring accessibility and reliability for data scientists and AI engineers.

Performance Optimization: Monitor and optimize data workflows for speed, scalability, and resilience in cloud and on-prem environments.

Collaboration: Work closely with cross-functional teams (data scientists, software engineers, project managers) to deliver end-to-end data solutions.

Continuous Improvement: Stay updated on emerging technologies in data engineering, big data, and cloud platforms; drive adoption of best practices.

Client Interaction: Support clients in understanding data requirements, present technical solutions, and provide expertise on data-driven strategies.

Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.

Experience: 3–5 years in data engineering, big data, or data platform development.

Technical Skills:

• Strong proficiency in Python and SQL; experience with distributed systems (Spark, Databricks).

• Cloud Platforms:

  • Microsoft Azure: Azure Data Factory, Azure Data Lake Storage, Microsoft Fabric,Azure Cosmos DB.

  • Other Clouds: AWS (Glue, RDS, Athena, Redshift, S3), GCP (BigQuery, Dataflow).

• Data Orchestration & Workflow: Airflow, Prefect, Dagster, ADF

• Data transformation: dbt

• Data Warehousing & Modeling: Snowflake, DuckDB, BigQuery.

• Streaming & Real-Time Processing: Kafka, Azure Event Hub.

• Visualization & BI: Power BI, Tableau.

• Familiarity with CI/CD pipelines (Azure DevOps, GitHub Actions) and containerization (Docker, Kubernetes).

• Infrastructure as Code: Terraform

 Analytical Thinking: Ability to design efficient data workflows and troubleshoot complex data issues.

Communication: Clear and concise communication skills for technical and non-technical stakeholders.

Ethical Standards: Commitment to data privacy, security, and compliance with relevant regulations.

Bonus: Experience with MLOps and DataOps.

Fluency in both written and spoken Dutch and English is required.

Opportunity to work on cutting-edge data and AI projects with diverse clients.

Collaborative and supportive work environment.

Continuous learning and professional development opportunities.

Competitive salary and benefits package.

Flexible working hours and work-life balance initiatives.

Data Architecture & Pipelines: Design, develop, and maintain scalable data pipelines and ETL processes to ingest, transform, and deliver structured and unstructured data.

Data Governance & Quality: Implement governance frameworks, data catalogs, and quality assurance processes to ensure compliance, integrity, and security of all data assets.

Data Integration & Storage: Manage data lakes, warehouses, and streaming platforms; optimize storage solutions for performance and cost-efficiency.

Enable AI & Analytics: Prepare and curate datasets for machine learning and advanced analytics projects, ensuring accessibility and reliability for data scientists and AI engineers.

Performance Optimization: Monitor and optimize data workflows for speed, scalability, and resilience in cloud and on-prem environments.

Collaboration: Work closely with cross-functional teams (data scientists, software engineers, project managers) to deliver end-to-end data solutions.

Continuous Improvement: Stay updated on emerging technologies in data engineering, big data, and cloud platforms; drive adoption of best practices.

Client Interaction: Support clients in understanding data requirements, present technical solutions, and provide expertise on data-driven strategies.

Opportunity to work on cutting-edge data and AI projects with diverse clients.

Collaborative and supportive work environment.

Continuous learning and professional development opportunities.

Competitive salary and benefits package.

Flexible working hours and work-life balance initiatives.

Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related field.

Experience: 3–5 years in data engineering, big data, or data platform development.

Technical Skills:

• Strong proficiency in Python and SQL; experience with distributed systems (Spark, Databricks).

• Cloud Platforms:

  • Microsoft Azure: Azure Data Factory, Azure Data Lake Storage, Microsoft Fabric,Azure Cosmos DB.

  • Other Clouds: AWS (Glue, RDS, Athena, Redshift, S3), GCP (BigQuery, Dataflow).

• Data Orchestration & Workflow: Airflow, Prefect, Dagster, ADF

• Data transformation: dbt

• Data Warehousing & Modeling: Snowflake, DuckDB, BigQuery.

• Streaming & Real-Time Processing: Kafka, Azure Event Hub.

• Visualization & BI: Power BI, Tableau.

• Familiarity with CI/CD pipelines (Azure DevOps, GitHub Actions) and containerization (Docker, Kubernetes).

• Infrastructure as Code: Terraform

 Analytical Thinking: Ability to design efficient data workflows and troubleshoot complex data issues.

Communication: Clear and concise communication skills for technical and non-technical stakeholders.

Ethical Standards: Commitment to data privacy, security, and compliance with relevant regulations.

Bonus: Experience with MLOps and DataOps.

Fluency in both written and spoken Dutch and English is required.