Data Engineer

Mid-Level Data Engineer

Location: London / Hybrid

Salary: Dependent on experience

Type: Permanent

I'm working with a growing wealth management business looking to bring on a Mid-Level Data Engineer to help build and maintain a reliable, scalable data platform.

The business is focused on improving the quality, consistency and usability of financial data flowing in from multiple sources. They're looking for someone with strong Python and SQL skills who enjoys solving data problems, building robust pipelines and ensuring information is accurate and fit for purpose.

This is a hands-on opportunity for an engineer who wants to take ownership of their work, contribute to technical decisions and develop their expertise within a fast-moving financial services environment.

The RoleYou'll be responsible for developing and maintaining data pipelines, transforming and normalising information from multiple sources, and implementing checks to ensure data is accurate and consistent across the platform.

Working closely with the wider team, you'll help improve data quality, troubleshoot discrepancies and optimise existing processes. The role would suit someone who has a solid grounding in data engineering and is ready to take greater ownership of pipelines and data models, without needing to come from a senior architectural background.

Key Responsibilities

Develop and maintain data pipelines using Python and SQL.

Work with PostgreSQL and Snowflake to ingest, transform and manage data from multiple sources.

Build and maintain data models, ensuring information is structured and normalised consistently.

Implement data quality checks, validation rules and reconciliation processes to identify missing data, duplicates and inconsistencies.

Investigate data discrepancies and build exception-handling processes to prevent recurring issues.

Support the integration of external data sources and APIs.

Contribute to data platform improvements, including Snowflake query optimisation and processing efficiency.

Apply Medallion Architecture principles to support structured data ingestion and transformation.

Monitor pipeline performance and reliability, helping to improve data lineage and visibility.

Work collaboratively with the team to ensure data is accurate, reliable and suitable for downstream applications and business decisions.What We're Looking For

Around 2–5 years' experience in Data Engineering or a similar technical role.

Strong SQL skills and practical experience using Python for data processing and transformation.

Experience building and maintaining data pipelines.

Exposure to Snowflake, PostgreSQL or similar database technologies.

Understanding of data modelling, data normalisation and working with multiple data sources.

Experience with data validation, reconciliation or data quality monitoring.

Familiarity with APIs and integrating external datasets.

Exposure to Medallion Architecture or similar modern data engineering frameworks.

A problem-solving mindset, with the ability to investigate issues and understand the underlying cause rather than simply fixing symptoms.

A proactive attitude and willingness to take ownership in a growing business.Desirable Experience

Previous experience in wealth management, investment management or financial markets.

Familiarity with equities, bonds, dividends or other investment-related data.

Experience optimising Snowflake queries and improving pipeline performance.

Understanding of data lineage, monitoring and exception management.The OpportunityThis is an opportunity to join a growing business where data quality is central to the product and customer experience. You'll have