Int. Data Engineer to support to build and automate databases for asset management data for a large banking client - 71915

Int. Data Engineer to support to build and automate databases for asset management data for a large banking client - 71915

LOB: GAM Business Analytics & Insights

Duration: 12 months (possibility of extension)

Location: Downtown Toronto - Hybrid (2x per week in office, Tues/Thurs)

The ideal candidate will have knowledge of current data technology and data engineering practices. This role will work with a team of data engineers to build and automate databases for asset management data, including investment, market, and sales data. Technical knowledge on database structure, maintenance, pipelines, and automation is essential. This role will also be accountable for database migration to upgraded technology and working with broader technology teams to access and automate new datasets. The ideal candidate will be collaborative and sociable and have stakeholder management skills. They will also work with data scientists and analysts to create and automate datasets and models for analytics.

Must-Have Skills:

• 4+ yrs of relevant experience and education

• SQL/Python/R/GIT experience

• Knowledge and experience in building solutions in data cloud platforms, including Snowflake, AWS, and/or Azure

• Experience in building and automating datasets and reports.

• Experience in proper data documentation and version control

• Experience in scheduling and automating advanced analytics solutions (e.g. machine-learning or generative AI)

• Proficient in data-driven decision-making and using insights to guide strategic actions.

Nice To Have Skill:

• Investment industry knowledge, including basic knowledge of sales and portfolio management.

• Previous banking experience

• Previous experience with Vermillion

Role Responsibilities:

• Database governance and maintenance, including schema structure, table creation, and ensuring database best practices and naming conventions.

• Building data pipelines to source across the data ecosystem.

• Working in different data platforms, including on-premises and cloud-based infrastructures.

• Communicating technical concepts in on-technical terms.

• Fostering strong internal and external networks and works across multiple teams to achieve business objectives.

• Collaborating with other team members to deliver analytics solutions.

• Collaborating with broader technology and cybersecurity teams to source data and keep in compliance with data security guidelines.

• Autonomously running data tests and identifying solutions. Escalating solutions to leadership as needed for approvals.

• Broader work or accountabilities may be assigned as needed.

Interview Process:

First Round - 30min in person interview with a panel of HM’s

Second Round - 30min MS Teams with a panel of HM’s and possible one-up HM