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Data Engineer

A Data Engineer is responsible for designing, building and maintaining an organization’s data infrastructure, ensuring that raw data is collected, processed, stored, and made available for analysis, reporting, business intelligence or machine learning. The role involves creating and managing data pipelines (ETL/ ELT), defining storage and access architecture, and ensuring data quality, scalability and performance.

Data Engineers work closely with analytics, development, DevOps, and management teams, providing the technical foundation necessary for data to effectively be leveraged.

Salary

The salary of a Data Engineer can vary depending on experience level and employment conditions. Actual compensation depends on skills and experience and may often include bonuses, flexible working hours, remote options and budgets for training or certifications.

Working hours

Typically full-time, with a standard work schedule (40 hours/ week). During implementations, migrations or data/ infrastructure related incidents, urgent interventions may be required, so flexibility and availability are advantageous.

Remote work possibility

Depending on the company and the project, the possibility of remote or hybrid work exists, with flexibility.

Types of employers

A data engineer can work in:

  • Software / tech/ application development companies

  • Organizations handling large volumes of data: financial services, retail/ e-commerce, telecom, healthcare, logistics, etc.

  • Firms operating in big data, analytics, AI/machine learning, data science

  • IT infrastructure, cloud and data service providers, including data warehouse/ data leaks

  • Large enterprises as well as startups of SMEs requiring modern data infrastructure

Responsibilities

  • Designing and implementing ETL/ ELT pipelines (data collection, transformation, loading) to integrate diverse data sources into a unified structure

  • Building and managing data warehouses, data lakes, or databases tailored to data volumes and types

  • Definind data architecture and storage/ access strategies for performance, scalability and security

  • Automating data flows and processing, including batch or streaming processing, transformations, validations, and data cleaning

  • Ensuring data quality, integrity, and consistency by implementing controls, validations and monitoring

  • Collaborating with analytics, data science, business intelligence, and DevOps teams to ensure the data infrastructure meets business needs

  • Optimizing the performance of data systems - queries, storage, response time, costs, scalability

  • Documenting data flows, architecture, and policies, maintaining and upgrading data infrastructure

Skills

Technical skills

  • Programming: e.g. Python, SQL, often also Java, Scala or other relevant languages

  • Strong knowledge of relational and/ or NoSQL databases, data warehousing and data lakes

  • Experience with ETL/ ELT, data pipelines, processing tools, orchestration, streaming/ batch processing

  • Familiarity with cloud computing and cloud-based data infrastructures, including storage, scaling and security services

  • Ability to design scalable and sustainable architectures that meet requirements for volume, security, and performance

Soft skills

  • Analytical thinking and attention to detail (data quality, data integrity, edge cases)

  • Ability to solve complex problems (architecture, scalability, consistency)

  • Effective collaboration and communication - to work with analysts, data scientists, DevOps, and management

  • Adaptability and continuous learning - the data ecosystem evolves rapidly

  • Responsibility and rigor - data is critical, and errors can have major consequences

Qualifications

  • Higher education in a relevant field: computer science, software engineering, information systems, mathematics/ statistics, or related areas

  • Practical experience with programming, databases, data pipelines, cloud and data warehouses/ lakes - demonstrated through projects, internships, or previous jobs

  • Knowledge of SQL and/ or other relevant programming languages; familiarity with ETL tools and cloud platforms

  • Preferred: experience managing large data volumes, Big Data, distributed systems or streaming - not mandatory for entry-level positions

What else you can do

  • Advancement to more complex roles: Senior Data Engineer, Lead Data Engineer, Data Arhitect, Big Data Engineer, Cloud Data Engineer

  • Specialization in tools and areas such as big data, streaming, data lakes, data governance, security and data platform design

  • Collaborating with Data Science/ ML teams to implement predictive and machine learning solutions - data engineering provides the foudnation

  • Participation in open-source projects, workshops, cloud/ data certifications and data conferences

  • Contributing to the definition of organizational data standards and policies, governance, quality, procedures, and documentation

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