Data Engineer Jobs in Denmark

Data Engineers are in high demand in Denmark due to the country’s rapidly growing tech sector and the increasing importance of data-driven decision-making across industries. Companies are investing heavily in digital transformation, requiring skilled Data Engineers to build, maintain, and optimize data infrastructure. With the rise of big data, AI, and machine learning, there is a strong need for professionals who can handle large volumes of data and ensure efficient data pipelines. This demand is fueled by Denmark’s thriving startup scene, multinational tech companies, and a strong focus on innovation.

Key Responsibilities of the Data Engineer

A Data Engineer is responsible for designing, building, and maintaining the infrastructure required for the effective collection, storage, and processing of large-scale data. Their primary focus is on ensuring data is reliable, available, and easily accessible to data analysts, data scientists, and other stakeholders within an organization. Here are the key responsibilities of a Data Engineer:

  • Design, build, and maintain scalable and reliable data pipelines that can handle large volumes of data from various sources.
  • Implement ETL (Extract, Transform, Load) processes to clean, transform, and structure data for analysis.
  • Integrate data from multiple sources, including databases, APIs, third-party platforms, and cloud services.
  • Ensure that the data is merged consistently and accurately for unified insights.
  • Design, develop, and maintain databases and data warehouses to support business requirements.
  • Optimize database performance and ensure data is stored efficiently.
  • Develop processes to ensure data accuracy, consistency, and reliability.
  • Implement data validation rules, monitoring systems, and quality checks to detect and resolve data issues.
  • Design and implement data models that define how data is stored and accessed.
  • Collaborate with data analysts and scientists to develop data models that align with analytical needs.
  • Optimize data processing workflows and queries to improve performance and reduce processing times.
  • Use indexing, partitioning, and other optimization techniques to enhance system efficiency.
  • Work closely with data scientists, analysts, and business stakeholders to understand data needs and translate them into technical solutions.
  • Provide support to data teams by ensuring they have access to the necessary data in the correct format.
  • Implement data security measures, including encryption, access control, and data masking.
  • Ensure compliance with data governance policies, privacy regulations, and industry standards (e.g., GDPR, HIPAA).
  • Monitor the performance and reliability of data pipelines and infrastructure.
  • Troubleshoot and resolve data-related issues promptly to minimize downtime.
  • Evaluate new tools, technologies, and methodologies that can improve data infrastructure and processing capabilities.
  • Stay updated with industry trends and integrate relevant technologies to enhance data engineering processes.
  • Document data workflows, data models, and infrastructure setups to ensure clear communication and reproducibility.
  • Create reports and dashboards to monitor system performance and data quality.
  • Automate repetitive tasks within data processing pipelines to improve efficiency and reduce manual errors.
  • Use scheduling tools like Apache Airflow or similar to manage data workflows.
  • Work with big data technologies like Hadoop, Spark, or other distributed computing platforms for large-scale data processing.
  • Implement strategies for managing unstructured data and handling real-time data streams.

These responsibilities highlight the technical and strategic role of Data Engineers in enabling data-driven decision-making within an organization. For those seeking IT jobs in Denmark, Data Engineering offers a dynamic career path with opportunities to work on advanced data solutions, contribute to innovative projects, and support businesses in leveraging data for strategic insights and decision-making.

Technical Skills Required for Data Engineer

Data Engineers need a broad set of technical skills to design, build, and maintain data infrastructure effectively. Here are the key technical skills required for a Data Engineer:

  • Python: Widely used for data manipulation, scripting, and building data pipelines.
  • SQL: Essential for querying and managing relational databases.
  • Java/Scala: Often used in big data processing frameworks like Apache Spark and Hadoop.
  • Bash/Shell Scripting: Useful for automating tasks and managing servers.
  • Relational Databases: Knowledge of SQL databases like MySQL, PostgreSQL, Oracle, and SQL Server.
  • NoSQL Databases: Experience with non-relational databases like MongoDB, Cassandra, DynamoDB, or Redis for handling unstructured data.
  • Data Warehousing: Skills in tools like Amazon Redshift, Google BigQuery, Snowflake, and Azure Synapse.
  • Apache Hadoop: Understanding of Hadoop ecosystem tools such as HDFS, MapReduce, and Hive.
  • Apache Spark: Proficiency in Spark for large-scale data processing and real-time analytics.
  • Kafka: Knowledge of Kafka for building real-time data pipelines and event streaming.

Experience with ETL (Extract, Transform, Load) tools such as Apache NiFi, Talend, Informatica, or Microsoft SSIS for data integration and transformation.

  • Understanding of data modeling concepts, including designing data schemas, star and snowflake schemas, and normalization/denormalization techniques.
  • AWS: Familiarity with services like S3, Redshift, Glue, Lambda, and EMR.
  • Azure: Experience with Azure Data Factory, Azure SQL Database, and Synapse Analytics.
  • Google Cloud Platform (GCP): Skills in BigQuery, Dataflow, and Cloud Storage.
  • Knowledge of orchestration tools like Apache Airflow, Luigi, or Prefect for managing complex workflows and scheduling data jobs.
  • Skills in working with RESTful APIs, web services, and data integration tools to connect and transfer data across systems.
  • Understanding of data privacy, security best practices, and compliance requirements (e.g., GDPR, HIPAA).
  • Implementing access controls, data encryption, and other security measures.
  • Knowledge of Docker and Kubernetes for containerizing applications and managing data workflows.
  • Experience with CI/CD tools like Jenkins, CircleCI, or GitLab CI for automating deployment pipelines.
  • Familiarity with monitoring tools like Grafana, Prometheus, or Datadog for tracking system performance.
  • Ability to optimize SQL queries, data workflows, and system configurations to enhance performance.

Understanding of tools like Tableau, Power BI, or Looker to create visual representations of data, though this is more typical for analysts.

Experience with tools like Great Expectations, Datafold, or custom validation scripts to maintain data quality and accuracy.

Data Engineer Salary Range in Denmark

The salary range for Data Engineers in Denmark varies by experience level:

  • Entry Level (1-3 years): DKK 701,423 to DKK 850,000 annually.
  • Mid Level (4-7 years): DKK 850,000 to DKK 1,015,084 annually.
  • Senior Level (8+ years): DKK 1,015,084 to DKK 1,238,402 annually.

The average salary for a Data Engineer in Denmark is approximately DKK 1,015,084 per year, with hourly rates around DKK 488.

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Top 5 Danish Cities for Data Engineer

Here are the top 5 Danish cities for Data Engineers:

  1. Copenhagen – The capital city and tech hub of Denmark, offering numerous opportunities in data-driven companies and startups.
  2. Aarhus – Known for its growing tech sector and presence of major IT companies and research institutions.
  3. Odense – A rapidly developing city with a strong focus on robotics, AI, and data technologies.
  4. Aalborg – Home to several tech firms and a vibrant academic environment fostering innovation.
  5. Esbjerg – An emerging tech city with increasing demand for data professionals, especially in the energy sector.

These cities offer vibrant job markets, innovation hubs, and opportunities for Data Engineers looking to advance their careers in Denmark.

How to Find Data Engineer Jobs in Denmark for English-Speakers

Here are unique tips to find Data Engineer jobs in Denmark as an English-speaker:

  1. Leverage International Job Portals: Use platforms like LinkedIn, Glassdoor, and Indeed, which frequently list English-friendly job postings in Denmark.

  2. Target International Companies: Look for roles at multinational companies or startups in Denmark that have English as their working language, such as Google, Microsoft, and Maersk.

  3. Join Tech Communities: Engage with Danish tech meetups, webinars, and Slack channels like Copenhagen Data Science Meetup to network with potential employers.

  4. Work with Recruitment Agencies: Seek specialized IT recruiters in Denmark who assist international professionals, such as Accuro and European Search Company.

  5. Utilize Danish Job Boards: Use English-friendly Danish job boards like Jobindex.dk and Workindenmark.dk to find localized opportunities.

These approaches will help you connect with the right employers and find suitable Data Engineer roles in Denmark.

Top 5 Technical Interview Questions Asked to Data Engineer

This question tests your understanding of data extraction, transformation, and loading along with your ability to enhance performance.

This question examines your knowledge of streaming data, tools like Apache Kafka, and real-time analytics.

Focus on indexing, query rewriting, and other techniques to improve database performance.

They look for methods to validate, cleanse, and monitor data integrity.

This tests your understanding of data storage, performance, and scalability strategies.

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