Senior Data Engineer

London is one of the world’s leading technology and financial centres, and at Fengxun Technology Solutions, we build dependable data platforms that help organisations make faster and better informed decisions.

We develop scalable systems that collect, process and organise large volumes of information from multiple business applications.

We believe that valuable data should be accurate, accessible and secure rather than trapped in disconnected systems.

We are looking for a Senior Data Engineer who can take ownership of our data infrastructure and help establish reliable engineering standards across our platforms.

You will design robust data pipelines, improve warehouse performance and work closely with software engineers, analysts and business stakeholders to transform complex information into trusted data products.

You are not simply moving information between systems. You are creating the technical foundation that allows teams to understand performance, automate reporting and make confident decisions.

You will also support less experienced engineers, contribute to architectural decisions and help maintain high standards of reliability, security and documentation.

Why Work With Us?

The Environment:

Our London based team provides a collaborative and technically focused working environment where engineers are trusted to solve complex problems and contribute to important platform decisions.

Data Engineering Standards:

You will not need to convince us that data quality matters.

We value reliable pipelines, clear data models, comprehensive documentation and engineering practices that prevent errors before they reach production.

Modern Data Platform:

Our technology environment includes cloud infrastructure, data warehouses, orchestration tools, streaming services and automated deployment workflows.

Business Impact:

The pipelines and platforms you develop will directly support operational reporting, product analytics, forecasting and strategic decision making.

Growth:

You will receive support for relevant professional certifications, technical training, conferences and continued development in cloud data engineering, platform architecture and information security.

Key Responsibilities:

Data Platform Ownership:

Design, maintain and improve scalable data infrastructure that supports reporting, analytics and operational applications.

End to End Pipeline Development:

Lead the process from source system assessment and data modelling through pipeline development, testing, deployment and monitoring.

Engineering Mentoring:

Support Junior and Mid level data engineers.

Conduct code reviews and provide practical feedback that improves technical quality, consistency and team capability.

Data Quality:

Do not rely on assumptions.

Introduce validation, testing and monitoring processes that identify incomplete, inaccurate or delayed data before it affects users.

Stakeholder Collaboration:

Present technical recommendations to analysts, product teams and business stakeholders.

You can explain why a particular data model or architecture is appropriate, not simply how it will be implemented.

Performance Optimisation:

Monitor pipeline and warehouse performance, identify bottlenecks and implement improvements that increase reliability and reduce processing costs.

Security and Governance:

Apply appropriate access controls, encryption and data retention standards while supporting compliance with relevant privacy and data protection requirements.

At least 6 years of professional data engineering or software engineering experience, including experience delivering complex data platforms in a production environment.

Programming Skills:

Strong experience with Python, SQL and at least one additional programming language used for data processing or backend development.

Cloud Knowledge:

Practical experience building and maintaining data solutions on Amazon Web Services, Microsoft Azure or Google Cloud Platform.

Data Technologies:

Strong knowledge of data warehouses, data lakes, extract transform load processes, application programming interfaces and distributed data processing.

Tooling:

Experience with technologies such as Apache Spark, Apache Kafka, Airflow, Databricks, Snowflake, BigQuery, Redshift or comparable platforms.

Engineering Practices:

Experience with source control, automated testing, continuous integration, continuous delivery, infrastructure as code and production monitoring.

Data Modelling:

Strong understanding of dimensional modelling, relational databases, schema design and methods for maintaining accurate and consistent datasets.

Communication Skills:

Strong professional English is required for technical documentation, stakeholder discussions and collaboration with engineering and analytics teams.

Mindset:

You are practical, analytical and quality focused.

You know when to improve an existing pipeline, when to redesign a data model and when to prioritise a reliable solution over unnecessary complexity.

Pulled from the full job description

• Free parking
• Company pension
• Private medical insurance
• On-site parking