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Hire a Data Engineer

Data Engineers help businesses build the infrastructure needed to support analytics, automation, and AI systems at scale. As organisations invest more heavily in cloud platforms and machine learning capability, demand is growing rapidly for engineers who can design reliable data pipelines, improve system performance, and support AI-ready data environments.

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What does a Data Engineer do?

A Data Engineer designs, builds, and maintains the systems that allow businesses to store, organise, and process data efficiently. Their role focuses on creating scalable infrastructure that supports analytics, reporting, automation, and machine learning environments.

Data Engineers commonly work on:

Data pipelines and ETL workflows
Cloud-based data platforms
Real-time data processing systems
Data warehousing solutions
Database optimisation and scalability
Integrating data across multiple applications and systems

They often work closely with data analysts, software engineers, and AI teams to ensure data is accessible, reliable, and structured effectively for business use.

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Need to hire a Data Engineer?

As businesses generate larger volumes of data, many discover their existing systems are no longer capable of supporting reporting, analytics, or AI initiatives effectively. Strong Data Engineers help organisations build scalable infrastructure that improves performance, reliability, and long-term flexibility.

Data Engineers create systems that move and process data efficiently across platforms, helping businesses reduce bottlenecks and improve operational visibility.

Modern Data Engineers commonly work with platforms such as AWS, Azure, Google Cloud, Snowflake, and Databricks to support scalable cloud infrastructure.

Poor data infrastructure can slow reporting, create inconsistencies, and increase technical debt. Strong engineers help improve stability, speed, and system efficiency.

Many organisations operate across multiple platforms and applications. Data Engineers help connect these systems so teams can access more accurate and consistent data.

Hiring Data Engineer roles? Ask about…

David Berwick, Adria Solutions

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ETL and ELT Pipelines SQL and Python Data Warehousing Apache Spark or Airflow Cloud Data Platforms (AWS, GCP, Azure) Data Lake Architecture Schema Design Data Quality Checks Streaming Data Data Governance SEE LIVE JOBS

Businesses typically hire Data Engineers when data becomes too large, fragmented, or operationally complex to manage effectively through existing systems or internal teams.

You may need to hire a Data Engineer if you are:

Scaling analytics or reporting capability
Building a cloud data platform or warehouse
Investing in AI or machine learning projects
Struggling with slow or unreliable reporting
Processing large volumes of operational or customer data
Integrating data across multiple systems or platforms

Many businesses also hire Data Engineers during digital transformation projects, cloud migrations, or periods of rapid product and data growth.

Strong Data Engineers combine software engineering expertise with deep understanding of data infrastructure, scalability, and system performance. The best candidates can design reliable pipelines while also supporting long-term analytics and AI capability.

A strong hire will typically have:

Strong SQL and programming capability
Experience with cloud data platforms and infrastructure
Knowledge of ETL workflows and pipeline development
Experience working with large-scale datasets
Understanding of scalability and database optimisation
Familiarity with tools such as Airflow, Spark, Kafka, and dbt
Strong systems thinking and problem-solving ability

The most in-demand candidates are often those who can support both operational systems and AI-ready infrastructure across cloud-based environments.

Why hiring Data Engineers can be challenging

While the demand for data skills continues to rise, many candidates focus on analytics or modelling rather than infrastructure. Data Engineers need a blend of software development experience, platform knowledge and a deep understanding of data systems, and that combination isn’t easy to find.

At Adria Solutions, we’ve spent years helping organisations build strong, scalable data teams. We know what makes a great Data Engineer and how to find them quickly.

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Data Engineer salary expectations

Data Engineer salaries continue to rise as businesses place greater emphasis on scalable cloud infrastructure, analytics, and AI-ready systems. In the UK, most permanent salaries range from £55,000 to £110,000+, while contract rates commonly fall between £500 and £950+ per day.

Higher salaries are typically associated with candidates who have experience in cloud-native platforms, large-scale pipeline engineering, real-time processing, and machine learning infrastructure. Demand remains especially strong across fintech, SaaS, ecommerce, and enterprise technology businesses.

Level	UK Salary Range	Contract Day Rate
Mid-level	£55,000 – £75,000	£500 – £700
Senior	£75,000 – £110,000+	£700 – £950+

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FAQs

Got questions? Find quick answers to the most common queries here.

AI systems rely heavily on clean, structured, and accessible data. Data Engineers build the infrastructure needed to support machine learning pipelines, model training, and scalable AI deployment.

Many Data Engineers work with cloud platforms and tools such as Snowflake, Databricks, Spark, Kafka, Airflow, dbt, AWS, Azure, and Google Cloud.

A Data Engineer focuses on building and maintaining the systems that store and process data, while a Data Scientist focuses more heavily on analysing data and developing predictive models.

Demand has increased significantly due to growth in AI, cloud migration, analytics, and real-time data processing. Experienced engineers with scalable cloud infrastructure expertise are particularly difficult to hire.

Businesses should look for candidates with strong experience in cloud platforms, pipeline development, scalability, database optimisation, and supporting analytics or AI environments in production systems.

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