Position Details:

Title: Junior Data Engineer

Duration: Full-time, Permanent

Location: Toronto, ON- Hybrid – 3x/week in office

Start Date: ASAP

Compensation and Benefits: $80-$90k & Benefits: Standard health, dental, vacation, and personal day package (aligned with firm policy)

Interview Process: 3 Rounds- Virtual and Inperson 

Day-to-Day Responsibilities:

• Design, build, and maintain data pipelines that integrate data from multiple sources (Bloomberg, Morningstar, Nasdaq, etc.).
• Support production systems, troubleshoot data issues, and automate workflows to increase efficiency.
• Enforce data governance and data quality standards in collaboration with internal stakeholders.
• Partner with data scientists, analysts, and product teams to ensure smooth delivery of data products.
• Participate in code reviews, documentation updates, and continuous improvement initiatives.
• Perform data analysis on structured and unstructured datasets to identify insights and issues.
• Stay current on modern data engineering tools, frameworks, and cloud technologies.

Must-Haves:

• Bachelor’s degree or college diploma in Computer Science, Engineering, or related field — ideally from University of Waterloo, University of Toronto, or Queen’s University
• 2–3 years of hands-on data engineering experience (recent graduates with strong co-op or project experience will also be considered).
• Strong programming skills in Python and SQL.
• Hands-on experience with AWS Data & Analytics stack: EMR, Glue, Lake Formation, Airflow, PySpark, Trino, Hudi, Iceberg.
• Understanding of Big Data concepts: change data capture, data quality, and data lineage.
• Familiarity with data governance principles.
• AWS Certified Data & Analytics Specialty (or equivalent).

Nice-to-Haves / Plusses:

• Experience with sustainable investing or ESG datasets.
• Prior experience in financial services, investment management, or capital markets.
• Familiarity with market data vendors (Bloomberg, Capital IQ, S&P, Morningstar).
• Exposure to Scala or additional data engineering frameworks.
• Understanding of enterprise data architecture and governance frameworks.