Future Tech Jobs
Future Tech Jobs
The backbone of modern data infrastructure. Building, maintaining, and optimising the pipelines that power data-driven decisions.
Data Engineers are the architects and builders of the data infrastructure that underpins modern businesses. They design, build, and maintain the data pipelines, warehouses, and lakes that enable organisations to store, process, and analyse vast amounts of data. Whether working in scaleups, research-heavy startups, or the larger consultancies, Data Engineers play a crucial role in ensuring that data is accessible, reliable, and secure.
A typical week for a Data Engineer is a mix of coding, testing, and collaboration with cross-functional teams.
Based on advertised midpoints across the 127 priced live listings on the board. Base salary only.
A typical UK progression for data engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.
Assists in building and maintaining data pipelines, with a focus on learning and supporting more senior team members.
Takes ownership of specific data pipelines and projects, ensuring they meet performance and reliability standards.
Leads the design and implementation of complex data infrastructure, mentoring junior engineers and collaborating with cross-functional teams.
Strategises and oversees the entire data infrastructure, driving innovation and best practices across the organisation.
There's no single route, but most people follow some version of these steps.
Start with foundational skills in SQL, Python, and data storage systems. Gain experience with ETL processes and data warehousing.
Work on building and maintaining data pipelines using tools like Apache Kafka and Apache Spark. Focus on efficiency and scalability.
Optimise data storage and retrieval processes to ensure high performance and reliability. Collaborate with data scientists and analysts.
Take ownership of complex data infrastructure projects. Mentor junior engineers and ensure best practices are followed.
Strategise the overall data infrastructure, driving innovation and best practices. Oversee the implementation of new technologies and methodologies.
This role involves building and scaling a modern data platform using GCP and dbt, focusing on data ingestion into BigQuery and developing analytics-ready models. You will work closely with BI Analysts to support company-wide reporting and self-serve analytics, contributing to the company's rapid growth and data-driven decision-making.
This role involves building and scaling a modern data platform using GCP and dbt, focusing on data ingestion into BigQuery and developing analytics-ready models. You will work closely with BI Analysts to support company-wide reporting and self-serve analytics, contributing to the organization's rapid growth and data-driven decision-making.
This role involves building and scaling a modern data platform using GCP and dbt, focusing on data ingestion into BigQuery and developing analytics-ready models. You will work closely with BI Analysts to support company-wide reporting and self-serve analytics, contributing to a high-growth education technology business.
This role involves designing, building, and maintaining scalable data pipelines within Microsoft Fabric Lakehouse environments. You will develop and optimize data ingestion and transformation workflows, create Python and PySpark processes, and support Power BI reporting and semantic models. As a senior member of the BI and data team, you will guide development standards, mentor developers, and ensure data solutions are scalable and aligned with enterprise data governance practices.
This role involves owning and developing a modern data platform, building and maintaining data ingestion pipelines into BigQuery, and designing analytics-ready dbt models. You will work closely with the BI Analyst to support company-wide reporting and self-serve analytics, contributing to a high-growth education technology business.
This role involves designing, building, and maintaining scalable data pipelines within Microsoft Fabric Lakehouse environments. You will develop and optimize data ingestion and transformation workflows, support Power BI reporting, and ensure strong data quality and governance standards. As a senior member of the BI and data team, you will also mentor developers and guide development standards.
This role involves owning and developing a modern data platform, building and maintaining data ingestion pipelines into BigQuery, and designing analytics-ready dbt models. You will work closely with the BI Analyst to support company-wide reporting and self-serve analytics, contributing to the company's rapid growth and data-driven decision-making.
The Lead Data Engineer will develop mission-critical data solutions for National Security clients, working with cutting-edge technologies such as AI/DS, Cyber, Cloud, DevOps/SRE, and Platform Engineering. Responsibilities include designing and implementing sophisticated data pipelines, ensuring data quality and security, and leading agile teams to solve complex, high-stakes challenges.
This role involves managing and monitoring the ETL pipeline for financial reporting, driving the migration to a cloud-native data platform, and collaborating with IT and business stakeholders to fix data issues and gather requirements. The position offers the opportunity to shape the cloud architecture and improve the efficiency of the finance function.
This role involves designing and building scalable cloud-based data platforms using Medallion Architecture, engineering data pipelines with Python and PySpark, and integrating data from multiple sources. The position offers the opportunity to work on complex data challenges in the London insurance market, collaborating with data architects, modellers, and business stakeholders to deliver modern cloud solutions.
This senior leadership role involves overseeing multiple teams of Data, DataOps, and MLOps Engineers. You will be responsible for defining and implementing a data engineering strategy, building AI-ready cloud-native data platforms, and driving large-scale data initiatives across the UK and Europe. The role focuses on strategic leadership, team development, and stakeholder engagement, with no hands-on technical work expected.
As a Data Engineer, you will play a pivotal role in shaping the client's data landscape by integrating Fabric, designing scalable data solutions, and collaborating with various stakeholders. Your responsibilities include building and maintaining data ingestion pipelines, implementing ETL/ELT processes, and ensuring strong data governance.
As a Principal Data Engineer, you will lead the technical direction and architecture for a modern, domain-oriented Data Mesh on Google Cloud Platform. Your responsibilities include defining project approaches, designing scalable data pipelines, and mentoring senior engineers to maintain high standards. This role offers a unique opportunity to shape the long-term engineering strategy and influence the Data Mesh journey in a 100% cloud-native environment.
This role involves designing, developing, and maintaining secure, high-quality data pipelines to support trusted reporting, analytics, and future AI/ML development. You will work closely with the Head of Data & Analytics to influence architecture, engineering standards, and governance within a co-managed delivery model, focusing on structured and semi-structured data from various internal systems.
As a Data Engineer, you will lead the migration of existing data pipelines to Databricks, ensuring seamless and scalable data solutions. You will collaborate with team members to build and maintain robust data infrastructure, troubleshoot issues, and contribute to the strategic roadmap for data engineering.
Data Engineers commonly use tools like Apache Kafka, Apache Spark, SQL, Python, and data warehousing solutions such as Amazon Redshift or Google BigQuery.
Collaboration with data scientists is crucial. Data Engineers need to understand the data needs of data scientists to build effective data pipelines and infrastructure.
Key skills include proficiency in SQL, Python, and data storage systems, as well as a strong understanding of ETL processes, data warehousing, and big data technologies.
The typical career progression is from Junior Data Engineer to Data Engineer, then Senior Data Engineer, and finally Principal Data Engineer, with increasing levels of responsibility and leadership.
For specific salary information, please refer to the salary section on this page, which is updated with the latest data from live job listings.
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