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About the Role

Partner with product owners and engineering teams to shape the technical direction of data engineering capabilities. Migrate file-based products to a unified, cloud-native data platform, architecting data pipelines, feed generation systems, and data delivery infrastructure. Serve as a technical thought leader, owning end-to-end data platform architecture and defining best practices for data governance, modeling, performance, and reliability.

Responsibilities

Lead and provide deep technical direction across data feeds and the data engineering function, guiding architectural decisions across platforms.
Architect the platform consolidation strategy, migrating legacy feed products onto a unified, governed, cloud-native architecture.
Design and implement scalable data delivery mechanisms for both file-based feeds and modern marketplace distribution platforms.
Drive DataOps maturity by establishing comprehensive data quality, monitoring, alerting, and CI/CD practices across the platform.
Influence technical strategy across teams by communicating architectural vision to both technical and non-technical stakeholders.

Requirements

9+ years of experience in data engineering, data platforms, or distributed systems.
Proven track record building and optimizing large-scale data pipelines on a major cloud platform (AWS preferred; Azure or GCP also accepted).
Strong experience with distributed or high-performance compute engines for large-scale data transformation.
Familiarity with frameworks such as Spark/PySpark, DuckDB, or similar modern engines, and the ability to evaluate trade-offs between them for different workloads.
Expert proficiency in SQL (Postgres, SQL Server, etc).
Strong development skills in Python (Python 3.x).
Strong hands-on experience with modern cloud data warehouses (e.g., Snowflake, Databricks, Redshift).
Demonstrated ability to influence engineering direction without direct management authority, mentor engineers, and drive alignment across teams.
Experience with containerization (Docker, Kubernetes).
Hands-on experience with cloud object storage (AWS S3, Azure Blob Storage, or Google Cloud Storage).
Knowledge of data lake and lakehouse architecture, including the implementation and use of open table formats like Delta Lake and Apache Iceberg.
Previous experience in highly regulated or financial services industries with stringent data quality and delivery SLA requirements.
Experience using agentic coding tools (e.g., GitHub Copilot, Claude Code, Cursor) to accelerate development workflows.

Skills

Data Engineering
Data Platforms
Distributed Systems
Cloud-native architecture
Data Pipelines
Feed Generation Systems
Data Delivery Infrastructure
Data Governance
Data Modeling
Performance Optimization
Data Reliability
DataOps
CI/CD
Spark/PySpark
DuckDB
SQL
Python
Snowflake
Databricks
Redshift
Docker
Kubernetes
AWS S3
Azure Blob Storage
Google Cloud Storage
Data Lake Architecture
Lakehouse Architecture
Delta Lake
Apache Iceberg
Agentic Coding Tools

Location

Toronto, ON

Work Type

Hybrid
4 days in Office

Experience Level

Principal
9+ years

Salary/Compensations

$112,583.00-$162,125.00

Benefits

20% Annual Incentive Target
Hybrid work environment
Tools and resources to engage meaningfully with global colleagues

About the Company

Morningstar Research, Inc. (Canada) Legal Entity