SENIOR DATA ENGINEER
Location: Toronto downtown business care, hybrid
Salary: Market rate ++
We are a dynamic, Toronto-based IT consulting boutique which strives for excellence in customer service and delivery in financial services and fintech industry. We focus on technology consulting, cloud computing, integration solutions, and enterprise data and analytics. With a team of highly knowledgeable business and technical experts, we provide strategic, mission critical solutions to our clients.
Responsibilities
Understand business requirements for data and information in the financial services industry.
Collaborate with cross-functional teams to deliver analytics solutions.
Design and implement scalable data pipelines and ETL processes.
Drive technology modernization and migration projects to modern platforms.
Develop and maintain data models and data warehouse infrastructure.
Develop high performing data pipelines in Microsoft SSIS, Azure Data Factory, or Databricks.
Develop database stored procedures based on Microsoft T-SQL and/or Oracle PL/SQL.
Develop data processing modules in the following programming languages: C#, Java, Python
Perform data profiling and data quality audit.
Optimize performance and reliability of data systems in cloud and on-prem environments.
Design automated unit-testing framework as per project requirement.
Integrate pipelines with Continuous Integration/Delivery framework as needed.
Produce estimates for analysis, design, development, and testing for data pipelines..
Ensure compliance with business, data, and technical requirements.
Ensure that client’s enterprise architecture standards, policies, and procedures are followed.
Requirements
Minimum Bachelors degree in Computer Science or Engineering.
Minimum 5 years experience in Data engineering and ETL development.
Strong problem solving, communication, and collaboration skills.
Strong knowledge in data analysis, database development, data warehousing life cycle and data integration methodologies (ETL, ELT, EAI, EII).
Strong SQL knowledge
Strong knowledge in Python for data processing and automation.
Hands-on experience with cloud-based data platforms (AWS or Azure).
Familiarity with AWS tools such as SageMaker, AWS Unified Studio, and other data services or familiarity with Azure Data Factory.
Experience in tech modernization/migration projects.
Familiar with capabilities of various platform services (e.g. Compute, Storage, Web, Developer, Integration, Data, Security, or Management) from the cloud vendors.
Working knowledge of one of data analytics tools (such as Microsoft Power BI).
Knowledgeable in both relational and dimensional data modeling (with both Kimball and Inmon approaches).
Knowledge in data management, REST-oriented API, Continuous Integration and Delivery (CI/CD) principles.
Business knowledge in financial industries (i.e. one or more in retail banking, commercial banking, capital markets, wealth management, insurance, pension fund, and fintech).
Ability to work independently and excel in a team environment.