Data Engineer H/F - REMOTE FLEXIBLE

Job Title: Data Modeler
Location: Toronto (Remote/ Hybrid)
Job Type: Contract for 12+ months
Reporting Line: SVP, Architecture
Salary Range: $95–$115 CAD per hour (negotiable)

Fulfillment IQ is a supply chain engineering and transformation company that helps brands, retailers, and 3PLs design, build, and scale high-performance logistics operations.

We work at the intersection of strategy, operations, and technology where we solve complex, real-world problems across warehouse design, automation, order management, transportation, and end-to-end supply chain execution.

Our teams combine deep domain expertise with strong technical capability, delivering outcomes through consulting, systems implementation, and proprietary platforms that accelerate time-to-value and reduce delivery risk.

We are seeking an experienced Data Modeler to design and implement the data models powering a multi- site warehouse intelligence platform on Google Cloud Platform (GCP). The ideal candidate will have a strong background in data modeling, dimensional modeling, and a deep understanding of the supply chain and logistics domains. The role requires a hands-on approach, with a focus on designing data
models that bridge multiple warehouse management systems, data lake house architectures, and real- time operational data stores.

6+ years of experience in data modeling roles, including logical, physical, dimensional, and domain modeling

• 3+ years of experience with Snowflake, including data engineering, data modeling, and data warehousing
• Supply chain/logistics/warehousing domain knowledge, including warehouse data: inventory lifecycle, order management, WMS transactions, shipping/receiving, labor tracking
• SQL expertise, including advanced query design, performance tuning, and complex joins across large datasets
• Experience with modern data lake house architecture
• Cloud data platforms: GCP (BigQuery, Cloud Storage, Cloud Spanner) or equivalent AWS/Azure experience with willingness to work in GCP
• CDC/event-driven data modeling expertise, including designing schemas for change data capture pipelines and streaming data
• Strong understanding of data governance, data quality, and data lineage

Experience with Blue Yonder (JDA/RedPrairie) WMS data structures and Oracle transactional schemas
MDM (Master Data Management) modeling experience, including hierarchical entity governance (org/site/system/region)
Experience designing JSON/semi-structured data models and configurable transformation schemas
Prior work in multi-tenant or multi-site data architectures (normalizing data across large-scale operational deployments with different configurations)
Familiarity with ML feature engineering and feature store design patterns
Knowledge of data catalog and metadata management tools

Experience with Google Cloud Spanner data modeling (wide-column/relational hybrid)
Understanding of GenAI/RAG data requirements (how LLMs consume structured operational data)
Exposure to Oracle GoldenGate or FiveTran CDC source schemas
Experience with Erwin, dbt, or similar data modeling/transformation tools

Design the canonical data model that normalizes data from multiple WMS systems (i.e., Model core warehouse entities, including inventory transactions, orders, shipments, ASNs, locations, SKUs, wave management, dock operations, labor events, and returns/RMA
Define Master Data Management (MDM) data models, including client, site, system, division, region hierarchies with governance rules
Design table schemas, including partitioning strategies, sort orders, schema evolution plans, and compaction policies
Create the bronze/silver/gold data layer architecture, including raw CDC events from WMS, cleansed and deduplicated domain tables, and aggregated business-ready datasets for reporting and ML Models
Design reporting models to replace a legacy Oracle-based analytics platform across a large multi-site deployment
Work with the BI team on Polaris catalog metadata standards and data dictionary
Design data schemas for the near real-time operational data layer, including inventory position, scan and validate events, exception/error state tracking, and wave progress and dock utilization
Model the data structures that feed GenAI operational assistants
Design the data models that feed Warehouse AI modules, including forecasting models, slotting/replenishment models, and pick optimization models
Define the feature store schema for ML model training and inference pipelines
Ensure ML data models align with the 5-minute Iceberg refresh cadence and real-time streaming layer

Understand existing WMS data sources, GCP/Snowflake architecture, and current data flows.
· Identify data quality gaps and normalization challenges.

Deliver first draft of the canonical data model and bronze/silver schemas.
· Draft Kafka event schemas and establish metadata documentation.

Deliver production-ready canonical model for at least one WMS and site.

• Finalize end‑to‑end bronze → silver → gold architecture with optimized performance.
• Publish full data dictionary and lineage.
• Deploy dimensional models for reporting and ML/AI data structures.
• Improve data quality and consistency across multi‑site ingestion.

Key Performance Indicators (KPIs):

• Canonical Model Completion & Adoption across sites/WMS sources.
• Data Quality Improvements (dedupe, consistency, completeness).
• Pipeline Readiness across bronze/silver/gold layers.
• MDM Model Implementation across clients/sites.
• Documentation & Metadata Accuracy (data dictionary, lineage).
• Query Performance Optimization in Snowflake/BigQuery.
• Enablement of ML/AI & Feature Store Pipelines.

we build long-term careers.

Whether you’re leading a client relationship, shaping a solution, or building internal capability, your decisions have visible impact.

You’ll help solve real operational challenges that directly impact global commerce, customer experience, and supply chain performance.
We invest in mentorship, leadership development, and continuous learning — supporting both vertical growth and long-term career progression.

We support remote and hybrid work models, flexible schedules, and trust our teams to manage their time responsibly.

From project wins to personal milestones, we recognize contribution, celebrate progress, and value consistency over heroics.

You’ll work alongside experienced consultants, engineers, product leaders, and commercial professionals who value ownership, transparency, and high standards.

Comprehensive health and dental coverage for you and your family (region-specific plans)

• Competitive paid time off (PTO), sick leave, and public holidays
• Flexible leave policies that respect local labor standards

Retirement & Financial Security

• Retirement savings programs and employer contributions
• Flexible Work
• Remote and hybrid work options
• Flexible working hours aligned to role and client needs

Internet and business travel reimbursements
· We are proud to be an equal opportunity employer and are committed to building a diverse, inclusive, and high-performing workplace.
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