Senior Data Engineer – Python & PySpark
- Montréal, QC
- Hybrid
- Posted Oct 24, 2026
- 1 position
Opens LinkedIn
- Employment type
- Contract
- Experience level
- Senior · 5+ years
- Posting language
- English
- Working hours
- 40 hours per week
- Seniority
- Mid-Senior level
- Application method
- Direct apply is available
Job summary
Build and optimize production-ready data pipelines using Python, PySpark, and related Spark technologies. Troubleshoot performance bottlenecks and translate business requirements into technical solutions while engaging with clients and stakeholders.
Job details
PYTHON + PYSPARK ENGINEERS — YOUR NEXT BIG DATA ENGINEERING OPPORTUNITY! Ready to take your data engineering skills into a high-impact environment with a globally recognized investment banking and financial services leader? Role: Senior Data Engineer – Python & PySpark Client: Global Investment Banking Leader Location: Montreal, QC, Canada (Hybrid) Experience: 5–7 Years Employment: Contract Core Technical Requirements • Strong Python programming and hands-on PySpark expertise • Spark SQL, DataFrames, and RDDs • Scalable data pipeline development and optimization • Spark performance tuning and troubleshooting • SQL, data warehousing, and cloud platforms (AWS/Azure/GCP) • Git, CI/CD, and clean coding practices • Strong client-facing communication and stakeholder management • Preferred: Apache Airflow and Agile/Scrum experience What Sets You Apart? You can build production-ready data pipelines, resolve complex performance bottlenecks, and translate business requirements into effective technical solutions while engaging directly with clients. Act now if this aligns with your expertise. Interested or know a strong match? DM me with your profile and contact details. #Hiring #DataEngineer #Python #PySpark #ApacheSpark #MontrealJobs #CanadaTech #DataEngineering
What you’ll do
Build and optimize production-ready data pipelines using Python, PySpark, and related Spark technologies. Troubleshoot performance bottlenecks and translate business requirements into technical solutions while engaging with clients and stakeholders.
Requirements
Requires 5–7 years of experience, strong Python and hands-on PySpark expertise, and knowledge of Spark SQL, DataFrames, RDDs, SQL, data warehousing, and cloud platforms. Also requires pipeline development and optimization, Spark performance tuning, Git, CI/CD, clean coding practices, and strong client-facing communication; Airflow and Agile/Scrum experience are preferred.
Listed skills
- SQL · Preferred
- CI/CD · Preferred
- Troubleshooting · Preferred
- Git · Preferred
- Stakeholder Management · Preferred
- Python · Preferred
Other relevant skills
Identified from the job description. Confirm important requirements above.
- Python
- PySpark
- Spark SQL
- DataFrames
- RDDs
- Data Pipeline Development
- Data Pipeline Optimization
- Spark Performance Tuning
- Troubleshooting
- SQL
- Data Warehousing
- Cloud Platforms
- Git
- CI/CD
- Client Communication
- Stakeholder Management
Job areas
- Data & Analytics
- Technology
- Software
- Finance & Accounting