At Sun Life, data is one of the most valuable strategic assets. As a Data Engineer, you will design, build, and maintain the robust data pipelines and infrastructure that power financial services, insurance products, and wealth management analytics. Your work directly impacts millions of global clients who rely on Sun Life for financial security and health solutions. By delivering clean, reliable, and timely data, you enable advanced predictive modeling, business intelligence, and critical decision-making processes across the organization.

This role is highly collaborative and sits at the intersection of software engineering, data science, and business strategy. You will work closely with data scientists, business analysts, and product owners to translate complex business requirements into scalable technical solutions. Whether you are migrating legacy systems to modern cloud environments, optimizing database performance, or building real-time data ingestion pipelines, your engineering choices will directly influence the agility and security of Sun Life's digital ecosystem.

The engineering culture at Sun Life prioritizes technical excellence, continuous learning, and a strong sense of ownership. Because you will be working with highly sensitive financial and personal health information, data governance, security, and pipeline reliability are paramount. This makes the role both technically challenging and deeply rewarding, as you will solve complex architectural problems while adhering to the highest standards of data integrity and compliance.

The following questions are representative of what you can expect during the Sun Life hiring process. These questions are drawn from real interview experiences and are designed to test your technical competency, problem-solving framework, and behavioral alignment. Use them to identify patterns in how Sun Life evaluates candidates rather than simply memorizing answers.

SQL & Database Engineering

These questions evaluate your ability to write clean, efficient database queries, optimize performance, and troubleshoot data retrieval issues under pressure.

• Write a SQL query to identify duplicate records in a large transactional table and explain how you would safely remove them.
• How would you optimize a slow-running SQL query that involves multiple complex joins and aggregations on partitioned tables?

To succeed in the Sun Life interview process, you must prepare to demonstrate a balanced mix of technical mastery, architectural thinking, and business acumen. The hiring team looks for engineers who do not just write code in isolation but understand how their pipelines drive value for the business and its customers.

Role-Related Knowledge – You must show a deep understanding of core data engineering principles, database internals, and cloud infrastructure. Be ready to discuss the pros and cons of different storage formats, query optimization techniques, and pipeline patterns.

Problem-Solving & Debugging – Interviewers will evaluate how you approach ambiguous technical challenges. Whether you are optimizing a SQL query or fixing a bug in Python, they want to see a structured, logical troubleshooting methodology rather than guesswork.

Business Value Alignment – Sun Life highly values engineers who can connect their technical implementations to business outcomes. You should be able to articulate why a project mattered, how it improved operational efficiency, and how it served the end-user.

Collaboration & Culture Fit – As a data engineer, you will collaborate with cross-functional teams daily. You must demonstrate strong communication skills, empathy, adaptability, and an alignment with Sun Life's collaborative, client-centric values.

The interview process for a Data Engineer at Sun Life is structured to be transparent, efficient, and candidate-friendly. It typically takes around three weeks from the initial application to the final decision. The process is designed to evaluate your technical capabilities, your behavioral alignment, and your ability to deliver business value through technology.

The journey begins with an initial HR phone screen, focusing on your background, career aspirations, and basic alignment with the role. This is followed by a technical screening stage, which may include a take-home technical assessment or a live coding session focused on SQL and Python. The final stage is a panel interview where you will meet with hiring managers, senior engineers, and business stakeholders to dive deep into your past projects, technical expertise, and cultural fit.

The timeline above outlines the typical progression of a candidate through the hiring pipeline. While the exact steps may vary slightly depending on the seniority of the role and the specific team, you should expect a consistent focus on both technical proficiency and behavioral alignment. Use this timeline to pace your preparation, ensuring you allocate sufficient time to practice coding, review cloud architecture patterns, and structure your behavioral stories.

To excel in the Sun Life interview, you must understand the specific competencies that interviewers are trained to evaluate. Each stage of the process targets a distinct set of skills.

SQL Query Optimization & Database Design

Database systems are the foundation of Sun Life’s data infrastructure. Interviewers will closely evaluate your ability to write performant SQL and design scalable database schemas.

Be ready to go over:

• Query Optimization – Understanding execution plans, identifying bottlenecks, and rewriting inefficient queries to reduce resource consumption.
• Data Modeling – Designing transactional (OLTP) and analytical (OLAP) schemas, including star and snowflake schemas.
• Indexing Strategies – Knowing when and how to use indexes to speed up data retrieval without unnecessarily degrading write performance.
• Advanced SQL Concepts (less common) – Window functions, recursive common table expressions (CTEs), and complex partition strategies.

Example scenarios:

• "You are given a query that takes several minutes to run on a partitioned table. Walk us through how you would diagnose the slowness and optimize the query."
• "Design a database schema to track customer insurance claims, ensuring historical changes to policy coverage are preserved over time."

Python Programming & Debugging

Python is the primary language used for scripting, automation, and data transformation at Sun Life. You will be tested on your coding hygiene, algorithmic thinking, and debugging skills.

Be ready to go over:

• Code Troubleshooting – Identifying syntax, logical, and performance errors in existing Python code and refactoring it for better maintainability.
• Data Manipulation – Using standard libraries and frameworks to parse, clean, and transform structured and semi-structured data.
• Error Handling & Logging – Building resilient scripts that handle API timeouts, schema drift, and unexpected null values gracefully.
• Advanced concepts (less common) – Memory management in Python, generator functions for processing large files, and parallel processing.

Example scenarios:

• "Review this Python function designed to ingest a CSV file into a database. Identify the bugs, fix them, and optimize the code to handle a 10GB file without running out of memory."
• "Write a script that calls a paginated REST API, extracts specific fields, flattens the nested JSON response, and writes the output to S3."

Cloud Infrastructure & ETL Pipelines

Sun Life leverages modern cloud platforms, specifically AWS and Azure, to host its data ecosystems. You need to demonstrate a solid understanding of how to build and orchestrate scalable data pipelines in the cloud.

Be ready to go over:

• Cloud Data Services – Utilizing cloud storage (S3, ADLS), data warehousing (Redshift, Snowflake), and compute services (EMR, Databricks).
• Orchestration – Designing workflows using tools like Apache Airflow or cloud-native orchestrators to manage pipeline dependencies.
• Data Integration Patterns – Implementing efficient batch loading and real-time streaming pipelines.
• Advanced concepts (less common) – Infrastructure as Code (IaC) using Terraform, and implementing CI/CD pipelines for data engineering code.

Example scenarios:

• "Describe how you would design an end-to-end pipeline to ingest daily transactional data from an on-premises database into an AWS data lake, detailing the services you would use."
• "How do you handle schema evolution in your data lake pipelines when a source system adds or modifies columns?"

Project Impact & Business Value

At Sun Life, technical solutions are only as good as the business value they deliver. Interviewers will dig deep into your past projects to understand your direct contributions and the business outcomes of your work.

Be ready to go over:

• Impact Quantification – Articulating the business metrics your technical work improved (e.g., reducing processing time by 40%, saving $50k in cloud costs, enabling a new business report).
• Technical Ownership – Demonstrating your personal contribution, decision-making process, and leadership within a project.
• Stakeholder Communication – Showing how you collaborate with non-technical partners to gather requirements and explain technical constraints.

Example scenarios:

• "Tell us about a data engineering project you led. What business problem did it solve, what architectural choices did you make, and what was the final impact?"
• "Describe a time when you realized a requested data pipeline would not deliver the value the business stakeholder expected. How did you handle that situation?"

As a Data Engineer at Sun Life, your daily work will be dynamic, collaborative, and highly impactful. You will be responsible for the entire lifecycle of data, from ingestion to consumption.

• Designing and Building Pipelines – You will write clean, modular, and scalable code to extract, transform, and load data from a wide variety of internal and external sources into central data platforms.
• Optimizing Data Platforms – You will continuously monitor, troubleshoot, and tune the performance of databases, data lakes, and query engines to ensure fast and cost-effective data access.
• Collaborating with Analytics Teams – You will partner with data scientists, business analysts, and report developers to understand their data needs and deliver optimized datasets, often supporting tools like Tableau and Power BI.
• Ensuring Data Governance and Security – You will implement strict data quality checks, access controls, and data masking techniques to protect sensitive financial and personal data in compliance with regulatory standards.
• Modernizing Legacy Systems – You will participate in strategic initiatives to migrate legacy on-premises data warehouses and batch workflows to modern, cloud-based data platforms on AWS or Azure.