Senior Data Engineer: ELT Pipelines, Snowflake & Streaming

Aspect Capital Limited; Greater London; Hybrid; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Bonus

Medical Insurance

Electric car scheme

Flexi-working

Cycle to work scheme

Family Friendly Policies

Job summary

Aspect Capital Limited, a London-based hedge fund, seeks a Senior Data Engineer to join the Data Engineering team. You will own end-to-end data ingestion, processing and distribution across tick, timeseries, reference and alternative datasets, working with a diverse tech stack on prem and in the cloud.

You will collaborate with quantitative developers, researchers and portfolio managers to design scalable pipelines, improve processing, and enable efficient data access across the organization.

Qualifications

• 5+ years working as a Software Engineer or Data Engineer
• Strong expertise in Python
• Well-versed in SQL and a good understanding of relational databases
• Confident working with Linux and Docker
• An excellent communicator, ability to communicate clearly with technical and non-technical colleagues
• Financial industry experience
• Java
• dbt
• Snowflake or similar cloud data warehouse
• Kafka
• AWS
• Familiar with major financial data vendor APIs

Responsibilities

• Developing ELT pipelines to ingest and transform datasets with Python, Snowflake and dbt
• Building a streaming platform to capture and aggregate large volumes of tick data
• Extending internal libraries to provide unified access to our entire data catalogue
• Consolidating legacy MATLAB systems onto our cloud-native technology stack
• Working closely with quantitative developers, researchers and portfolio managers

Skills

Python

SQL

Linux

Communication

Financial industry experience

Java

APIs

Tools

dbt

Snowflake

Kafka

AWS

Docker

Job description

Aspect Capital Limited, a London-based hedge fund, seeks a Senior Data Engineer to join the Data Engineering team. You will own end-to-end data ingestion, processing and distribution across tick, timeseries, reference and alternative datasets, working with a diverse tech stack on prem and in the cloud.

You will collaborate with quantitative developers, researchers and portfolio managers to design scalable pipelines, improve processing, and enable efficient data access across the organization.