Hiring the right Data Engineer is essential for companies that depend on accurate, accessible and well-structured data. These professionals build the pipelines, platforms and data flows that help analytics, product, business and technology teams work with information they can trust.
At NBS, we help companies recruit Data Engineers with the right technical background, seniority and practical experience for the role. We focus on understanding your data environment, the maturity of your platform, the tools already in place and the level of ownership expected before starting the search.
Need to hire a Data Engineer?
Tell us what kind of data profile your company needs and we will help you define a focused and realistic search.
Data Engineer Recruitment is a specialised hiring service focused on finding professionals who design, build, maintain and improve the systems that move, store and organise data inside a company. Depending on the team and the technical environment, this can include Data Engineers, Senior Data Engineers, Big Data Engineers, Analytics Engineers, Cloud Data Engineers, Data Platform Engineers or Data Warehouse Engineers.
We review the technical requirements of the role, the level of seniority, the data stack, the platform maturity, the pipeline architecture and the responsibilities expected from the candidate. Some companies need experience with SQL, Python, ETL or ELT processes, data warehouses, data lakes, data modelling, orchestration, data quality or cloud data platforms.
Other roles may require knowledge of Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Spark, Kafka, AWS, Azure or Google Cloud. The goal is not only to find someone who has used data tools, but to identify candidates who understand how data engineering supports reporting, analytics, product decisions and business operations.
Looking for data engineering talent with real platform experience?
We can help you clarify the role, understand the market and identify candidates aligned with your data stack.
Need a realistic view of the Data Engineer market?
We can help you assess the role, the salary range and the type of candidates available before starting the process.
We can help companies recruit Data Engineers, Senior Data Engineers, Big Data Engineers, Analytics Engineers, Cloud Data Engineers, Data Platform Engineers and Data Warehouse Engineers. The exact profile depends on your data stack, platform maturity, team structure and level of ownership required.
A Data Engineer usually needs experience with SQL, Python, data pipelines, ETL or ELT processes, data warehouses, data modelling, orchestration and data quality. Depending on the role, knowledge of Snowflake, Databricks, BigQuery, Redshift, dbt, Airflow, Spark, Kafka, AWS, Azure or Google Cloud may also be relevant.
We review their previous data engineering experience, the type of platforms they have worked with, the scale of their projects, their technical stack, their level of autonomy and their ability to work with analytics, product, business or technology teams. We also look at motivation, communication, salary expectations and fit with the company.
Yes. Senior Data Engineer recruitment usually requires a more precise search because the role may include architecture decisions, platform ownership, data quality responsibility, mentoring, stakeholder communication and close collaboration with analytics or business intelligence teams.
A Data Engineer usually builds and maintains the pipelines, systems and platforms that make data available and reliable. A Data Analyst usually works with that data to create reports, dashboards and business insights. Both roles can work closely together, but their responsibilities are different.
A Data Engineer usually focuses on data infrastructure, pipelines, storage, quality and availability. A Data Scientist usually works on analysis, statistical models, experimentation or machine learning. In many teams, Data Engineers provide the foundations that Data Scientists need to work with reliable data.
Data Engineering is a broad technical field with many possible combinations of tools, responsibilities and seniority levels. Hiring becomes difficult when the role is not clearly defined, when the company expects one person to cover too many areas, or when the required experience is tied to a very specific data platform.
