Data Engineer

Data Engineers design, build, and maintain the technical infrastructure and data pipelines that enable efficient collection, storage, and processing of data for analysis and decision-making.

Description

Data Engineers are responsible for creating, optimizing, and maintaining the architecture, pipelines, and databases that enable the seamless flow of data from various sources to data warehouses and storage systems. They design ETL (Extract, Transform, Load) processes to clean, transform, and integrate data, ensuring its quality and accessibility for Data Scientists and Data Analysts. Data Engineers collaborate with cross-functional teams to ensure data availability and support data-driven initiatives within organizations.

Qualification

Data Engineers typically qualify for their positions through a combination of education, skills, and experience. Many Data Engineers have a background in Software Engineering, Computer Science, Business Computing or Engineering. Due to high demand, an increasing number of individuals is transitioning to Data Engineering and other data-related positions through specialized bootcamps.

Skills

Data pipelines
Data Warehouse
Data Lake
IT infrastructure
Cloud Platforms
Data architecture
ETL & ELT
Query Languages

Data Engineer salary

Junior Data Engineer
45.000 – 62.000€

Senior Data Engineer
58.000 – 78.000€

Lead Data Engineer
75.000 – 104.000€

Note: The actual salary depends strongly with organisation size, industry and location.
skillmap-data-engineer

Tasks of a Data Engineer

  • Communication: Collaboration with Data Scientists, Data Analysts and other stakeholders to align data accessibility with business requirements.
  • Data Collection: Extracting data from various sources, including databases, APIs, streaming platforms, and external sources.
  • Data Integration: Combining data from different source systems.
  • Data Architecture: Creation of a scalable data architecture for storage and processing (ETL + ELT).
  • Data Transformation: Data preprocessing and transformation to ensure that the data is clean, structured, and suitable for analysis.
  • Data Quality and Security: Implement processes and tools to ensure data quality, integrity, and security.
  • Monitoring: Maintenance of the data pipelines, transformation and sorage.

Data Engineer Tools

  • ETL (Extract, Transform, Load) Tools: Compose, schedule, and monitor data flows with Apache Airflow and Apache NiFi
  • Database Systems: Oracle, MS SQL Server, MySQL, PostgreSQL, MondoDB, Apache Hadoop and Apache Spark.
  • Data Warehouses: Cloud-based data warehousing solutions like Amazon Redshift, Google BigQuery, Snowflake.
  • Cloud Platforms: Scalable infrastructure to store and process data with Amazon Web Service (AWS), Google Cloud and Microsoft Azure.
  • Programming Languages: Developing ETL pipelines and data processing applications with Python, Java and Scala.
  • Container and Orchestration: Containerizing applications and services with Docker, container orchestration and deployment. with Kubernetes.
  • Version Control Systems: Tracking changes and collaborating on code with git, GitHub, GitLab or Bitbucket.

Data Engineer career development 

The career as a Data Engineer starts as an individual contributor with technical expertise and domain knowledge, but without people responsibility. Depending on the company, distinctions are made between junior and senior levels. Larger companies offer technical leadership positions for Data Engineer (e.g., Staff Data Engineer and Principal Data engineer). As alternative to technical leadership roles, some Data Analysts opt for leadership positions as People Leads (e.g. Team Lead, Engineering Manager, Chapter Lead) or Product Leads (e.g. Product Manager, Product Owner). Additionally, there are also hybrid leadership roles with responsibilities across various domains.

Tech Lead Positionen
Product Lead Positionen
People Lead Positionen
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