Position: Data Modeler / Data Engineer
Location: Toronto, On – Hybrid
Job Description:
- Design and maintain JSON-based data models that support business workflows and optimize query patterns for both transactional and analytical use cases.
- Lead MongoDB schema evolution, including versioning, backward compatibility, and migration strategies aligned with changing data requirements.
- Apply best practices for indexing, partitioning/sharding, and performance optimization across MongoDB collections.
- Build and maintain data ingestion and curation pipelines using Azure Databricks, Azure Data Factory, and Azure Synapse.
- Develop and optimize PySpark pipelines for scale, performance, and cost efficiency within cloud environments.
- Collaborate with analytics teams and business stakeholders to curate high-quality datasets for downstream reporting and analytics.
- Ensure end-to-end compliance with enterprise data governance standards, including privacy, access controls, metadata management, and audit readiness.
Must-Have Skills
- MongoDB & JSON Data Modeling: Strong hands-on experience designing JSON/XSD/JSON schemas, managing schema evolution, versioning, and migration strategies with a focus on performance and data quality.
- Azure Data Engineering: Advanced SQL and PySpark (Spark SQL, performance tuning) with hands-on experience in Azure Databricks, Azure Data Factory (ADF), and Azure Synapse for ingestion and curation pipelines.
- Governance & Stakeholder Collaboration: Proven ability to work with business and analytics partners, translate requirements into scalable data solutions, and ensure adherence to governance, privacy, and metadata standards.
Regards
Patrick Fernandez
Talent Acquisition Group - Strategic Recruitment Manager