Senior Business Data Engineer

Job Description

Senior Data Engineer | Sun Life

The Tone:
This is a full-time role at Sun Life, located in Toronto with a hybrid work model. Sun Life’s Client Experience Office is a key pillar of its data and analytics strategy, focused on building enriched data products for deep insights. This role is indispensable for assembling these data products, which fuel numerous analytical models used across various business units in Canada, helping to identify new opportunities and understand client behavior.

The TL;DR
• Role: Full Time
• Type: Hybrid
• Location: Hybrid – Toronto
• Pay: $85000–$128000 yearly
• Team: Data Enablement Team, reporting to Manager of Data Engineering
• Mission: Assemble data products that fuel analytical models for business insights, supporting Sun Life Canada’s Data and Analytics growth.
• Tech Stack: AWS, Snowflake, Python, Pyspark, SQL, Parquet, Iceberg, Tableau, Quicksight, Streamlit

What You’ll Actually Do
• Design: Design and engineer data models that represent key business entities such as Advisor, Client & Product.
• Quality: Ensure a high level of data quality and data integrity are maintained across data sources for business data assets.
• Convert: Convert complex business and technical rules into logic for data flows and data pipelines.
• Collaborate: Work with Data Science teams to implement data products for various use cases.
• Document: Standardize metadata into a common glossary along with the necessary documentation for data consumers.

The Must-Haves
• Background: Senior individual contributor bridging business consumer requirements and data engineering practices to support the overall Business Data Strategy. Undergraduate degree in Computer Science, Mathematics, Engineering, or equivalent is required; a graduate degree in business or quantitative science is strongly preferred.
• Experience: 3-5 years hands-on experience working on major Cloud technologies and programming languages for data processing and analytics; 3+ years experience with AWS and Snowflake platforms for building robust ETL pipelines, Catalog management, Data Quality, and Governance; 5 years hands-on experience with relational database systems, relational models, dimensional models, and analytical models; 3-5 years experience with engineering data products as input to various analytical models; 3-5 years building feedback loops between model deployment and its data; experience working with very large datasets.
• Skills: Proficiency in Python, Pyspark, and SQL; demonstrated experience in cost and performance optimization of Spark and SQL jobs; sound understanding of Data Management principles (data warehousing, data quality, master data management) and Data Governance principles; sound understanding of data modelling; proven ability to extract, conform, and integrate a variety of operational data sources into production-grade data products; excellent verbal and written communication skills.
• Bonus: Experience with popular data formats like Parquet and Iceberg; experience in BI Tools like Tableau, Quicksight, or Streamlit; understanding and embrace of Agile Operating Model; understanding of AI concepts and Machine learning with familiarity of AI services of AWS and Snowflake platforms; knowledge of fundamental statistical models & techniques; knowledge and experience working with DevOps technologies; experience working in a large financial services organization.