Data Engineer at eHealth4everyone

• As a Data Engineer specializing in data lakes, data warehouses, and ETL, you will be responsible for designing, implementing, and maintaining our data infrastructure.
• You will work closely with data scientists, analysts, and other stakeholders to ensure seamless data flow, high-quality data, and accessibility for analytical and operational use cases.

Key Responsibilities:

• Design, build, and maintain scalable data lake and data warehouse architectures to store structured and unstructured data.
• Develop and manage ETL (Extract, Transform, Load) processes to ingest data from various sources into the data lake and data warehouse.
• Ensure data quality, data governance, and data security practices are implemented and maintained.
• Collaborate with data scientists and analysts to understand data requirements and provide solutions for data access and analysis.
• Optimize data storage and retrieval performance.
• Monitor and troubleshoot data infrastructure issues, ensuring high availability and reliability.
• Implement and maintain data catalog and metadata management tools.
• Stay updated with the latest trends and technologies in data engineering, data lakes, and data warehouses.

Qualifications:

• Bachelor’s degree in Computer Science, Information Technology, or a related field.
• 3+ years of experience in data engineering or a similar role.
• Strong experience with data lake technologies such as AWS S3, Azure Data Lake, Google Cloud Storage, or similar.
• Proficiency in ETL tools and processes (e.g., AWS Glue, Apache NiFi, Talend).
• Experience with big data processing frameworks like Apache Spark or Hadoop.
• Knowledge of data warehousing concepts and technologies (e.g., Amazon Redshift, Google BigQuery, Snowflake).
• Experience with SQL and NoSQL databases.
• Familiarity with data governance and data security best practices.
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills.

Preferred Qualifications:

• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with containerization and orchestration tools like Docker and Kubernetes.
• Knowledge of data catalog and metadata management tools (e.g., AWS Glue Data Catalog, Apache Atlas).
• Experience with data visualization tools and techniques.
• Relevant certifications in data engineering or cloud platforms.