Data Engineer
- Montreal, QC, Canada
Contract
Applications open in a new tab.
Job details
- Job type
- Contract
- Apply by
- Experience
- Experience: 5–7 years of professional experience in data engineering.
- Posting language
- English
- Posted
Original job description
Job Title: Data Engineer (Pyspark & Python)
Location: Montreal, QC (Hybrid)
Type: Contract
Job Overview:
We are seeking a skilled Senior Data Engineer with 5 to 7 years of experience specializing in Python and PySpark. In this role, you will design, build, and optimize robust data pipelines while serving as a primary technical point of contact for clients. The ideal candidate bridges the gap between complex data engineering and strategic client communication.
Key Responsibilities
- Data Pipeline Development: Design, develop, and maintain scalable data processing pipelines using Python and PySpark.
- Client Engagement: Act as the technical face of the team, gathering requirements, presenting data solutions, and managing stakeholder expectations.
- Optimization & Performance: Tune Spark jobs for optimal performance, troubleshooting bottlenecks in large-scale data environments.
- Collaboration: Partner with cross-functional teams, including data scientists, analysts, and project managers, to deliver business-critical data products.
Required Skills & Qualifications
- Experience: 5–7 years of professional experience in data engineering.
- Core Technical Skills: Strong proficiency in Python and hands-on expertise with PySpark (Spark SQL, DataFrames, RDDs).
- Client-Facing Skills: Proven experience in client-facing roles, with excellent verbal and written communication skills to articulate technical concepts to non-technical stakeholders.
- Data Ecosystems: Solid understanding of SQL, data warehousing concepts, and cloud data platforms (AWS, Azure, or GCP).
- Software Practices: Familiarity with Git, CI/CD pipelines, and writing clean, maintainable code.
Preferred Qualifications
- Experience working in an Agile/Scrum environment.
- Knowledge of orchestration tools like Apache Airflow.
More about this job
Responsibilities
Design, develop, maintain, and optimize scalable data pipelines using Python and PySpark, including tuning Spark jobs and troubleshooting performance bottlenecks. Serve as a technical point of contact for clients, gather requirements, present solutions, manage stakeholder expectations, and collaborate with cross-functional teams to deliver data products.
Requirements
Requires 5–7 years of data engineering experience, strong Python proficiency, and hands-on PySpark expertise, including Spark SQL, DataFrames, and RDDs. Candidates should have client-facing communication skills, knowledge of SQL, data warehousing and cloud platforms, and familiarity with Git and CI/CD; Agile/Scrum and Apache Airflow experience are preferred.
Skills
- Python
- PySpark
- Spark SQL
- DataFrames
- RDDs
- SQL
- Data Pipeline Development
- Data Warehousing
- Cloud Data Platforms
- AWS
- Azure
- GCP
- Git
- CI/CD
- Apache Airflow
- Agile/Scrum
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Consulting
Keywords
- Data Engineering
- Python
- PySpark
- Apache Spark
- Spark SQL
- DataFrames
- RDDs
- Data Pipelines
- Data Processing
- Spark Job Optimization
- SQL
- Data Warehousing
- Cloud Data Platforms
- AWS
- Azure
- GCP
- Git
- CI/CD
- Apache Airflow
- Agile
- Scrum
- Client Engagement
- Stakeholder Management
- Cross-Functional Collaboration
- Data Products