Senior Data Engineer / Developer - Python, SQL, Azure - Investment Management

We are supporting a leading organisation within the investment management sector who are looking for a Senior Data Engineer to work on a proprietary investment data integration product.

The platform is an enterprise ETL and integration layer that connects to leading front-to-back investment management systems and delivers standardised, high-quality investment data into a modern cloud environment.

This role requires a highly hands-on engineer with strong Python, SQL, Azure and Databricks experience, alongside deep enterprise data integration expertise within Asset & Investment Management.

INSIDE IR35

HYBRID WORKING 3 DAYS IN LONDON

Responsibilities :

1. Design, develop and optimise ETL pipelines using Python and SQL
2. Build and maintain scalable data pipelines leveraging Azure and Databricks (Spark-based processing)
3. Build new integrations into enterprise investment platforms (Aladdin, CRD, Simcorp Dimension, etc)
4. Build and maintain data pipelines within Azure-based infrastructure
5. Enhance and refactor existing data ingestion and transformation logic
6. Perform detailed data mapping across complex investment data sets
7. Transform and normalise portfolio, holdings, transaction, and reference data
8. Troubleshoot cross-system data inconsistencies
9. Improve performance, scalability and reliability of data workflows
10. Work closely with Data BAs and investment stakeholders to interpret data models and requirements

Experience :

1. Strong hands-on development experience in Python
2. Advanced SQL development skills , including performance tuning and complex query optimisation
3. Proven experience building and maintaining ETL / data integration pipelines
4. Strong experience with Databricks , including Spark-based data processing and optimisation
5. Experience developing within Microsoft Azure environments (Data Factory, storage, orchestration, or similar services)
6. Strong understanding of data modelling and relational database design
7. Experience working with investment management data sets such as:
8. Portfolio & holdings data
9. Transactions & cash movements
10. Security master & instrument reference data
11. Positions & valuations
12. Benchmarks
13. Client & mandate hierarchies
14. Experience integrating with enterprise investment platforms (OMS, PMS, IBOR, ABOR or similar)
15. Ability to interpret and map complex financial data structures
16. Strong analytical and problem-solving skills
17. Experience of working within Asset & Investment Management