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AI
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Hays

Data Engineer

Montreal, QC, Canada

Contract

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Job details

Job type: Contract
Apply by: Dec 2, 2026
Experience: 3–5 years of experience in Data Engineering or Software Engineering.
Posting language: English
Posted: Nov 2, 2026

Original job description

Python / PySpark Data Engineer

Job Type: Contract (12 Months)

Location: Montreal, QC (Hybrid – 2 Days Onsite)

Team: Trading Data / Consumer Data Team

Reporting Manager: VP, Data Engineering

Language Requirement: English

Your New Company

Our client is a leading global financial services organization seeking a Python / PySpark Data Engineer to join a strategic data engineering team supporting client data confidentiality and enterprise data monitoring initiatives.

This team is responsible for building and enhancing a critical platform used for client data protection, data usage monitoring, regulatory reporting, and enterprise-wide data governance. The successful candidate will work within a global team of approximately 70 professionals located across Montreal, New York, and India.

Your New Role

As a Python / PySpark Data Engineer, you will play a key role in building scalable data pipelines, onboarding new data sources, and enhancing a strategic data monitoring platform used for client and regulatory reporting.

You will collaborate with data engineers, developers, and business stakeholders to ensure data is processed accurately, efficiently, and reliably while supporting business-critical initiatives across multiple asset classes and business functions.

Key Responsibilities

Design, develop, and maintain scalable data pipelines using Python, PySpark, and Databricks.
Develop complex data transformation and processing solutions using SQL and Snowflake.
Build ingestion frameworks and transformation pipelines for multiple data sources, including operational databases, application telemetry, and user activity data.
Onboard new applications, databases, and datasets into the monitoring platform.
Implement data quality, reconciliation, validation, and monitoring controls.
Troubleshoot pipeline failures and optimize data processing performance.
Support regulatory, audit, and business reporting requirements through reliable data delivery.
Partner with engineers and stakeholders across Montreal and New York on strategic platform enhancements.
Contribute to platform scalability, maintainability, and operational excellence initiatives.

What You'll Need to Succeed

Required Qualifications

3–5 years of experience in Data Engineering or Software Engineering.
Strong hands-on development experience with Python.
Experience building data pipelines using PySpark.
Hands-on experience with Databricks.
Strong SQL development experience.
Experience working with Snowflake or comparable cloud data warehouse technologies.
Experience integrating and processing data from multiple heterogeneous data sources.
Strong debugging, analytical, and problem-solving skills.
Excellent communication and collaboration abilities.

Nice to Have

Experience with MongoDB.
Experience with KDB or time-series databases.
Exposure to AI/ML workloads.
Experience with data quality frameworks, reconciliation controls, and regulatory reporting environments.
Financial services or capital markets experience.

What Will Make You Successful

Strong business mindset with the ability to understand data challenges beyond pure development.
Ability to work effectively with globally distributed teams.
Comfortable operating in a highly regulated and data-sensitive environment.
Strong communication skills and ability to collaborate with both technical and business stakeholders.
Ability to think creatively and propose scalable solutions to complex data challenges.

What You Need to Do Now

If you're interested in this opportunity, please submit an updated copy of your resume.

If this role isn't quite right for you but you're exploring new opportunities within Data Engineering, Analytics, AI/ML, or Cloud Data Platforms, please contact us for a confidential career discussion.

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More about this job

Responsibilities

Design, build, and maintain scalable data pipelines and ingestion frameworks using Python, PySpark, Databricks, SQL, and Snowflake, onboarding diverse data sources into a strategic monitoring platform. Implement data quality and monitoring controls, troubleshoot and optimize pipelines, and support regulatory reporting while collaborating with global teams and business stakeholders.

Requirements

Requires 3–5 years of data or software engineering experience, strong Python, PySpark, Databricks, and SQL skills, and experience with Snowflake or a comparable cloud data warehouse. Candidates should be skilled in integrating heterogeneous data sources, debugging, and collaboration; financial services, MongoDB, KDB, AI/ML, and regulatory reporting experience are advantageous.

Skills

Python
PySpark
Databricks
SQL
Snowflake
Data Pipeline Development
Data Ingestion
Data Transformation
Data Quality
Data Validation
Data Reconciliation
Pipeline Troubleshooting
Performance Optimization
MongoDB
KDB
Time-Series Databases

Visa sponsorship

Not detected in the job text

Keywords

Python
PySpark
Databricks
SQL
Snowflake
MongoDB
KDB
Time-Series Databases
Data Engineering
Software Engineering
Data Pipelines
Data Ingestion
Data Transformation
Data Quality
Data Reconciliation
Data Validation
Data Monitoring
Regulatory Reporting
Client Data Confidentiality
Data Governance
Operational Databases
Application Telemetry
User Activity Data
Cloud Data Warehouses
AI/ML
Financial Services
Capital Markets
Pipeline Optimization

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