• Partner with Portfolio Management, Asset Allocation, Trading, Performance, Risk, Research, and other investment teams • Develop and maintain analytical data products supporting investment workflows • Translate financial and analytical requirements into scalable data solutions • Manage performance, attribution, time-series, holdings, positions, exposures, and aggregated analytics datasets • Ensure investment datasets are accurate, validated, timely, and well-governed • Own analytical product lifecycles from data ingestion and transformation through delivery and continuous evolution • Define requirements, priorities, operating expectations, and success measures with stakeholders • Design scalable data models and transformation pipelines • Develop Power BI and Streamlit analytical experiences • Build and maintain ETL/ELT pipelines for investment processes and recurring analytical workflows • Implement data quality controls, reconciliation, monitoring, and operational runbooks • Contribute reusable dbt models, standards, templates, documentation, lineage, and semantic-layer components • Support cloud-native platform modernization and AI-enabled analytical workflows • Diagnose issues across data dependencies, transformation logic, orchestration, and reporting layers

Requirements Strong Python development skills Solid understanding of data engineering, analytics engineering, data modeling, and best practices Experience building scalable ETL/ELT pipelines and analytical data models using dbt or comparable frameworks Experience with transformation logic, testing, documentation, lineage, and reusable modeling practices Experience with Google Cloud Platform and BigQuery Experience with Prefect, Dagster, or Airflow Familiarity with GitHub, code reviews, CI/CD concepts, Docker, and modern software development practices Experience building Power BI solutions,



semantic models, and analytical applications Understanding of data quality, validation, reconciliation, monitoring, and governance Solid understanding of investment and financial analytics concepts Ability to understand investment workflows and translate business and financial requirements into scalable solutions Experience working with financial datasets or investment analytics is highly desirable 5+ years of relevant experience for intermediate candidates; 8+ years for senior candidates Experience at the intersection of finance, analytics, data engineering, and technology Experience building data products, analytical solutions, modern reporting capabilities, or production-grade data pipelines Demonstrated ability to deliver and support production-grade data and analytics solutions Advanced proficiency in French; daily communication with English- and French-speaking clients and partners across Canada via email and phone calls Undergraduate or master’s degree in a relevant field preferred, not required

Core Competencies Demonstrates expertise in developing and maintaining analytical data products, building scalable ETL/ELT pipelines, and ensuring data quality and governance. Proficient in translating financial requirements into data solutions while collaborating with investment teams and stakeholders.

Highest-signal resume keywords Python Development ETL/ELT Pipeline Development Data Engineering Power BI Solutions Google Cloud Platform

ATS Optimization Keywords Hard Skills Data Modeling Analytical Data Products Transformation Logic Data Quality Controls Investment Analytics Dbt Framework BigQuery Prefect Dagster Airflow

Soft Skills Collaboration Communication Problem-Solving

Industry Keywords Investment Workflows Financial Datasets Analytics Engineering Data Governance Operational Runbooks

Tools & Technologies Power BI Streamlit GitHub Docker CI/CD

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📌 Senior Analyst, Quantitative Data Science (Winnipeg)
🏢 Jobtailor
📍 Winnipeg