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Added 11 mins ago
BS
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- Full-time
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ON · Ontario, Canada
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Nityo Infotech

Data Modeler

Toronto, ON, Canada

Contract

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Job details

Job type: Contract
Apply by: Nov 25, 2026
Posting language: English
Posted: Oct 26, 2026

Original job description

Role: Data Modeler

Location: Toronto Canada (onsite )

The Senior Data Modeller/Engineer will support Enterprise Payments’ operational data store by combining advanced JSON/MongoDB data modeling with cloud-based data engineering on the Rahona platform. The role is split 50/50 between data modeling and data engineering, working closely with data analytics teams and Payments partners. Success in this role means accelerating Payments’ ability to onboard new data pipelines, improve data quality, and comply with governance standards.

Although responsibilities are divided between data modelling and data engineering, workload allocation varies based on project priorities. Some projects will require ingestion into the operational data store, some will require only ingestion and curation into the data analytics Rahona platform, and some will require both.

Role Summary and Responsibilities

For MongoDB Data Modeller:

Design JSON schemas that support business workflows and query patterns.
Lead schema evolution and manage versioning/migration strategies.
Ensure MongoDB collections follow best practices for indexing, partitioning, and performance.
Collaborate with BSAs to understand the data requirements and how those translate into schemas that can be used for both transactional and analytic purposes.

For Rahona Data Engineer:

Build and maintain ingestion and curation pipelines in Databricks, ADF, and Synapse.
Optimize PySpark pipelines for scale, performance, and cost efficiency.
Collaborate with TD Analytics teams to curate datasets and support downstream analytics.
Ensure adherence to TD governance (PIA, DAC, metadata standards) during ingestion and curation.

REQUIRED SKILLS:

MongoDB data modeller (JSON data modelling and schema creation)

5+ years of experience in JSON-based data modelling.

Lead schema design to ensure data quality and performance.

Create XSD and JSON schemas.

Lead MongoDB schema evolution, versioning, and migration.

Rahona Data Engineer (data analytics ingestion and curation)

Advanced proficiency with SQL query language.

Advanced proficiency in PySpark, including Spark SQL and performance optimization.

Minimum 2+ years of experience with Azure Cloud Development such as Azure Databricks (primary platform for PySpark), Azure Data Factory (ADF), Azure Synapse

Strong knowledge of data ingestion, ETL processes, and orchestration frameworks for cloud environments. Ability to design and manage data pipelines - extracting, transforming, and loading data - especially in cloud environments.

Soft Skills

Ability to work with business partners to gather data requirements.

Strong analytical thinking, problem-solving, and troubleshooting abilities.

Self-motivated and proactive, with a keen eye for identifying opportunities for improvement and efficiency.

Excellent communication and documentation skills.

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More about this job

Responsibilities

Design and evolve MongoDB JSON schemas to support business workflows, query patterns, data quality, and performance. Build and optimize cloud-based ingestion and curation pipelines using Databricks, Azure Data Factory, and Synapse while supporting analytics and governance requirements.

Requirements

Requires at least five years of JSON-based data modeling experience, including schema creation and MongoDB schema evolution, versioning, and migration. Also requires advanced SQL and PySpark skills, at least two years of Azure development experience, and the ability to gather business requirements and troubleshoot data pipelines.

Skills

JSON Data Modeling
MongoDB
Schema Design
Schema Evolution
XSD
SQL
PySpark
Spark SQL
Azure Databricks
Azure Data Factory
Azure Synapse
Data Ingestion
ETL
Data Pipeline Orchestration
Data Governance
Data Quality

Visa sponsorship

Not detected in the job text

Keywords

MongoDB
JSON
XSD
Schema Design
Schema Evolution
Schema Versioning
Schema Migration
Indexing
Partitioning
SQL
PySpark
Spark SQL
Azure Cloud
Azure Databricks
Azure Data Factory
Azure Synapse
Data Ingestion
ETL
Data Curation
Data Pipelines
Orchestration
Operational Data Store
Data Analytics
Data Quality
Data Governance
PIA
DAC
Metadata Standards
Performance Optimization
Payments

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