Senior Data Engineer

hoopp; Toronto; On-site; CAD 110,000 - 170,000; Full time

Benefits offered by this job

Pension plan

Full health & dental benefits

Wellness programs

Learning & development

Job summary

HOOPP's Information Technology teams seek a Senior Data Engineer to design and build data foundations on AWS, turning raw data into trusted insights and AI-ready assets for pension analytics.

You will contribute to the Enterprise Data Platform, building scalable pipelines and establishing engineering standards while collaborating with data scientists and business partners to power AI-enabled decision making.

Qualifications

• 7+ years in data engineering with production AWS data solutions.
• Hands-on Snowflake experience including AI capabilities (Cortex, Snowpark).
• Strong SQL and Python for distributed data processing at scale (Spark preferred).
• Experience with unstructured data and AI/ML data patterns.
• IaC, Git, and CI/CD practices for data workloads.

Responsibilities

• Build scalable data pipelines on AWS across structured and unstructured data.
• Transform data into AI-ready assets and support AI use cases.
• Architect lakehouse patterns on S3 with open table formats.
• Set engineering standards, perform reviews, and mentor teammates.
• Ensure quality, lineage, and observability in all pipelines from day one.

Skills

SQL

Python

Spark

AWS

Snowflake

Data pipelines

CI/CD

Terraform

Git

Tools

Snowflake

S3

Glue

Lambda

Athena

Terraform

CloudFormation

Job description

Why you'll love working here:

• high-performance, people-focused culture
• our commitment that equity, diversity, and inclusion are fundamental to our work environment and business success, which helps employees feel valued and empowered to be their authentic selves
• learning and development initiatives, including workshops, Speaker Series events and access to LinkedIn Learning, that support employees' career growth
• membership in HOOPP's world class defined benefit pension plan, which can serve as an important part of your retirement security
• competitive, 100% company-paid extended health and dental benefits for permanent employees, including coverage supporting our team's diversity and mental health (e.g., gender affirmation, fertility and drug treatment, psychological support benefits of $2,500 per year, parental leave top-up, and a health spending account).
• optional post-retirement health and dental benefits subsidized at 50%
• yoga classes, meditation workshops, nutritional consultations, and wellness seminars
• the opportunity to make a difference and help take care of those who care for us, by providing a financially secure retirement for Ontario healthcare workers

Job Summary

HOOPP's Information Technology teams deliver innovative technology solutions to help build a stronger financial future for the healthcare community. As a Senior Data Engineer, you will design and build the data foundations (spanning structured and unstructured data on AWS) that turn raw information into trusted insight and AI-ready assets to support our teams in delivering on the pension promise.

Our Enterprise Data Platform team builds and operates the cloud data platform that powers analytics, reporting, and AI across HOOPP. As a Senior Data Engineer, reporting to the Senior Director, Enterprise Data Platform, you will build scalable pipelines on AWS across both structured and unstructured data, stand up the ingestion and retrieval patterns behind our emerging AI use cases, and set the engineering standards the broader data team builds on.

We cultivate a culture that emphasizes innovation, collaboration, practicality, and the courage to challenge the status quo. This role is well suited to someone who combines technical depth with curiosity, sound judgment, product thinking, systems thinking, and a willingness to test new ideas. You will look beyond immediate symptoms, identify underlying needs, and develop solutions that are secure, sustainable, and valuable in delivering the pension promise. Success in this role requires the ability to navigate ambiguity, ask thoughtful questions, and turn emerging technologies into practical solutions that deliver measurable business impact.

What you will do:

• Build the data foundations that power HOOPP's AI ambitions by transforming structured data, documents, text, and other unstructured content into trusted, accessible assets that fuel AI models, search, and retrieval capabilities.
• Take AI and analytics ideas from whiteboard to production with data scientists, AI engineers, and the business. We prototype fast, prove what works, and retire what doesn't.
• Use Snowflake as a core data warehouse and AI platform, applying capabilities such as Cortex AI to support scalable analytics and emerging AI use cases.
• Architect cloud-native pipelines on AWS that move structured and unstructured data at scale.
• Shape our lakehouse on Amazon S3 by using open table formats, thoughtful data models, and metadata that makes the platform genuinely easy for others to build on.
• Set the engineering standards the broader data team builds on, and raise the bar through design reviews, code reviews, and mentorship.
• Bring engineering rigor to data through automated testing, infrastructure-as-code, and CI/CD that make releases boring and reliable.
• Build quality, lineage, and observability into every pipeline from day one, so trust in the data is the default.

What you will bring:

• 7+ years of data engineering experience, combined with 3+ years building production data solutions on AWS.
• Hands-on depth with the AWS data stack - for example S3, Glue, Lambda, Athena, Step Functions, and EventBridge.
• Hands-on experience with Snowflake, including its AI and generative AI capabilities (such as Cortex AI, Snowpark, and vector search).
• Strong SQL and Python, with experience building distributed data processing at scale (Spark preferred).
• Demonstrated experience working with unstructured data (documents, text, images, or audio) in support of AI or machine learning use cases.
• Exposure to AI and generative AI data patterns such as embeddings, vector stores, and retrieval-augmented generation.
• Experience with lakehouse architectures and open table formats (Iceberg, Delta, or Hudi), as well as dimensional modelling for analytics.
• Working knowledge of infrastructure-as-code (Terraform or CloudFormation), Git, and CI/CD for data workloads.
• Ability to navigate ambiguity, ask thoughtful questions, and turn emerging technologies into pra