Junior Data Engineer

Capital Economics•Toronto, ON

1d•Hybrid

About The Position

Capital Economics is one of the world's leading independent economic research companies. Our goal is to provide our clients with original, timely and insightful analysis supported by solid economic thinking. We are hiring a Junior Data Engineer for our Economics Data and Modelling team. You will help us build the database and pipelines that power our research, dashboards, and a growing commercial data product used by our clients. This role is based in our London office, with hybrid working available. We will also consider applications from candidates who are able to work from our Toronto office. You will be joining a small, collaborative team where you will learn new skills, own your work, and help shape the development of the database and wider data infrastructure.

Requirements

Coding experience in Python, particularly for data manipulation and working with APIs (pandas, requests or similar).
Knowledge of relational databases such as PostgreSQL / MySQL / SQL Server.
Familiarity with SQL and query optimisation.
Experience using Git for version control.
Understanding of Linux.
A good undergraduate degree in computer science, economics, or a related quantitative subject.

Nice To Haves

Exposure to cloud environments (AWS or Azure) and containerisation (Docker).
Familiarity with workflow orchestration tools such as Prefect or Airflow.
Understanding of how to structure and optimise data for use in visualisation tools such as Plotly or Dash.
Experience with Power BI.
An interest in macroeconomics.

Responsibilities

Designing and implementing ETL pipelines to fetch and process data from a range of external sources.
Deploying and scheduling pipelines on our cloud infrastructure using Prefect and AWS.
Supporting the ongoing development and maintenance of the databases that underpins our interactive dashboards and economic research.
Building data quality checks into pipelines and carrying out periodic reviews to identify anomalies, gaps and unexpected revisions.
Supporting the rollout of new interactive dashboards by building the data layer behind them, including writing efficient queries and preparing data so dashboards run quickly and reliably.
Presenting your work to technical and non-technical stakeholders.