Closed listing
Senior Data Engineer – Modeling / Python / PySpark
- Mississauga, ON, Canada
Contract
This opening has closed.
The original listing is kept below for reference.
Job details
- Job type
- Contract
- Closing date
- Experience
- 8+ years of experience in Data Engineering or a closely related field.
- Posting language
- English
- Posted
Similar jobs hiring now
Search current openingsOriginal job description
Archived listing. The details below describe a past opening.
Initially remote but then onsite from Mississauga, ON.
ONSITE INTERVIEW MUST
We are seeking a Senior Data Engineer to design, develop, and implement scalable data solutions and processing pipelines. The ideal candidate will have strong hands-on experience in data modeling, SQL, Python, and PySpark/Spark, with the ability to work closely with development, application, and business teams to translate data requirements into effective technical solutions.
Key Responsibilities
- Design and develop scalable data solutions, pipelines, and data processing frameworks.
- Create and maintain logical and physical data models in collaboration with developers and technical stakeholders.
- Translate business and application requirements into effective data models and data structures.
- Develop and implement data processing solutions using Python and PySpark/Spark.
- Write and optimize complex SQL queries for data processing and analysis.
- Implement data solutions across data warehouses and data lakes.
- Optimize data processing performance, data quality, and reliability.
- Collaborate with application, development, and business teams to understand data requirements and deliver scalable solutions.
- Support data initiatives related to analytics, Artificial Intelligence, and Machine Learning.
- Contribute to data governance, quality, and best practices across data solutions.
Required Qualifications & Skills
- 8+ years of experience in Data Engineering or a closely related field.
- Strong hands-on experience with Data Modeling, including logical and physical data models.
- Strong SQL development and optimization skills.
- Hands-on Python development experience.
- Strong hands-on experience with PySpark/Spark, including data processing and pipeline development.
- Experience working with Data Warehouses and/or Data Lakes.
- Strong understanding of data engineering concepts, data processing, and scalable data solutions.
- Ability to collaborate effectively with technical and business stakeholders.
Preferred Qualifications
- Experience with Generative AI (GenAI) or Machine Learning initiatives.
- Exposure to or hands-on experience with Google ADK.
- Experience with cloud platforms such as GCP, AWS, or Azure.
- Experience with ETL/ELT frameworks and modern data engineering technologies.
- Previous experience in the Banking or Financial Services industry is highly preferred.
More about this job
Responsibilities
Design and build scalable data solutions, pipelines, and processing frameworks, including logical and physical data models, using Python, PySpark/Spark, and optimized SQL. Collaborate with technical and business teams to deliver data warehouse and data lake solutions, improve performance and quality, support analytics and AI/ML initiatives, and contribute to data governance practices.
Requirements
Requires 8 or more years of data engineering experience, with hands-on expertise in data modeling, SQL, Python, and PySpark/Spark. Candidates should understand data warehouses or data lakes and scalable data processing, and be able to collaborate with technical and business stakeholders; cloud, ETL/ELT, GenAI or ML, Google ADK, and banking experience are preferred.
Skills
- Data Modeling
- SQL
- Python
- PySpark
- Apache Spark
- Data Engineering
- Data Pipelines
- Data Warehousing
- Data Lakes
- ETL/ELT
- Data Processing
- Data Quality
- Data Governance
- Generative AI
- Machine Learning
- Google ADK
Visa sponsorship
Not detected in the job text
Categories
- Data & Analytics
- Technology
- Software
- Finance & Accounting
Keywords
- Data Engineering
- Data Modeling
- Logical Data Models
- Physical Data Models
- SQL
- Python
- PySpark
- Apache Spark
- Data Pipelines
- Data Processing Frameworks
- Data Warehouses
- Data Lakes
- ETL/ELT
- Data Quality
- Data Governance
- Analytics
- Artificial Intelligence
- Machine Learning
- Generative AI
- Google ADK
- GCP
- AWS
- Azure
- Banking
- Financial Services