Data Engineer

Role: Data Engineer – Data & Analytics Platform

Location: London, 3 days per week in the office

Salary: £60,000–£65,000 + 15% bonus + benefits

Purpose: Design, build and maintain scalable data architectures, cloud data warehouses and robust data pipelines.

Tech: Python (Pandas, NumPy, Streamlit), SQL, dbt, ETL/ELT, Apache Airflow, Cloud (AWS, Azure, GCP), Snowflake, BigQuery, Git, Docker, Kubernetes

Industry: Financial Data / Commodities / Market Intelligence

At this time, sponsorship is not on offer.

Immersum are supporting the growth of a specialist technology business looking to strengthen its Data Engineering capabilities. You will work closely with technical and business stakeholders to design, build and maintain scalable data architectures, improving how data is collected, organised and made available across the business. This is an opportunity to work with complex transactional and market data, supporting the development of data-driven products and analytics capabilities.

The Company:

An established technology-led business operating within a specialist financial data market. They are continuing to invest in their data capabilities, developing scalable data infrastructure and solutions that support data analytics, modelling and the delivery of data-driven products.

The Role:

The successful candidate will take ownership of building, maintaining and improving data architectures, cloud-based data warehouses and ingestion pipelines.

Working across structured and unstructured data from multiple sources, you will play an important role in ensuring data is accessible, reliable and fit for purpose. You'll collaborate with Data Scientists and wider stakeholders, supporting everything from exploratory data analysis and statistical quality controls through to the development and scaling of new data products.

You'll also be involved in improving data governance, quality and integrity, ensuring solutions are built using best practices and can transition from proof of concept into scalable production environments.

The Tech & Requirements:

• Python: Strong proficiency, including experience with Pandas, NumPy and Streamlit.
• SQL: Advanced skills in complex querying, data manipulation and transformation.
• dbt: Experience building and maintaining data models within cloud-based data warehouses.
• Data Engineering: Strong understanding of data architecture, ETL/ELT processes, ingestion pipelines and data modelling.
• Cloud Platforms: Experience with cloud environments such as AWS, Azure or Google Cloud Platform.
• Data Warehousing: Experience with platforms such as Snowflake and BigQuery.
• Workflow Orchestration: Experience with Apache Airflow or similar data pipeline tools.
• Version Control: Experience using Git and collaborative development practices.
• Software Delivery: Understanding of automated builds, testing, releases and agile development processes.

Desirable Experience:

• Experience with Docker, Kubernetes and containerisation.
• Experience building and deploying APIs.
• Knowledge of data governance, certification, security and compliance.
• Experience working with financial, transactional or market data.
• Exposure to Machine Learning, quantitative finance or supporting Data Science teams.
• Experience with data visualisation tools such as Power BI or Tableau.
• A degree in Data Engineering, Computer Science or a related discipline.

Interview Process:

1. First stage – Interview with senior technical and data stakeholders.
2. Second stage – Technical assessment.
3. Final stage – Interview with senior leadership.

If this looks of interest, please click apply to find out more!