Data Engineer – Banking / Financial Domain
Onsite data engineer (5–7 yrs) in Toronto for a banking/financial client, designing and maintaining scalable data pipelines using Databricks, Python, SQL, Azure database technologies, and Oracle, with data cleansing, troubleshooting, and optional AI/ML-adjacent work.
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 5+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5–7 years of experience in data engineering or data development
- Strong hands-on experience with Databricks
- Experience working with Azure database technologies
- Strong experience with Oracle Database and SQL
- Proficiency in Python programming for data processing and automation
- Experience with ETL/ELT processes, data integration, and data transformation
- Strong analytical, troubleshooting, and problem-solving skills
- Good communication and collaboration skills
- Must be available for in-person interview in Toronto (onsite role)
Nice to have
- Exposure to AI, machine learning, or AI-enabled data solutions
- Experience with Spark, PySpark, Delta Lake, or Azure data services
- Experience optimizing data pipelines and processing large datasets
Full description
Job Title: Data Engineer – Banking / Financial DomainLocation: Toronto, ON (onsite) -
In-person Interview is must
Experience: 5–7 Years
Open to Contract & Fulltime
Job Summary:
We are looking for an experienced Data Engineer with strong hands-on expertise in Databricks, Azure database technologies, Oracle, and Python. The ideal candidate should have experience designing, developing, and maintaining scalable data pipelines and data processing solutions to support business reporting, analytics, and data integration requirements.
Key Responsibilities:
- Design, develop, and maintain scalable data pipelines for data ingestion, transformation, and integration.
- Work extensively with Databricks for data processing, transformation, and analytics.
- Develop and maintain data workflows using Python and SQL.
- Integrate data from multiple sources, including Azure databases and Oracle databases.
- Write optimized queries and implement efficient data transformation logic.
- Perform data cleansing, validation, and quality checks to ensure accuracy and consistency.
- Troubleshoot data pipeline failures and resolve performance and data processing issues.
- Collaborate with business analysts, data architects, and other engineering teams to understand data requirements.
- Follow best practices for data security, governance, performance optimization, and documentation.
- Support data solutions involving AI or machine learning where applicable.
Required Skills and Qualifications:
- 5–7 years of experience in data engineering or data development.
- Strong hands-on experience with Databricks.
- Experience working with Azure database technologies.
- Strong experience with Oracle Database and SQL.
- Proficiency in Python programming for data processing and automation.
- Experience with ETL/ELT processes, data integration, and data transformation.
- Strong analytical, troubleshooting, and problem-solving skills.
- Good communication and collaboration skills.
Preferred Skills:
- Exposure to AI, machine learning, or AI-enabled data solutions.
- Experience with Spark, PySpark, Delta Lake, or Azure data services.
- Experience optimizing data pipelines and processing large datasets.
Ideal Candidate Profile: A hands-on Data Engineer with strong Databricks, Python, Azure database, and Oracle experience. AI experience will be an added advantage.