10+ years in data architecture/data engineering roles, with experience in platform-scale design.
Proven production implementations of Microsoft Fabric solutions in real operational environments.
Deep hands-on expertise in data engineering, Data Factory pipelines, and real-time analytics/event streams.
Strong capabilities in architecture across diverse data types: transactional, telemetry/events, documents, geospatial, time series.
Experience in implementing and governing data modeling standards and data governance practices.
Responsibilities
Architect and implement Fabric solutions for data engineering, focusing on Spark, Data Factory pipelines, and real-time analytics.
Standardize Fabric patterns for ingestion, transformation, and serving across various workloads.
Produce and maintain the target-state architecture for a Fabric-based data platform.
Define domain-oriented data product patterns to curate and reuse shared datasets.
Establish governance models covering classification, retention, and auditability.
Benefits
100% remote position for candidates based in Canada.
Prefer candidates from EST/CST time zones.
Full Job Description
Job Description PLEASE NOTE:
It is a 100% Remote position in Canada.
Candidates Preferred from EST/CST Time Zone.
Key Responsibilities:
Microsoft Fabric Enablement (Hands-on Delivery + Standardization)
Architect and implement Fabric solutions for data engineering (Spark), Data Factory pipelines, and real-time analytics / Event streams aligned to digital product needs.
Build and standardize Fabric patterns for ingestion, transformation, and serving across workloads including operational analytics, near-real-time, batch, and data science/ML.
Create repeatable reference implementations for common digital product scenarios (IoT telemetry, time series, transactional + event fusion, documents, geospatial).
Unified Data Platform Architecture (Target State + Roadmap)
Produce and maintain the target-state architecture for Fabric-based data platform capabilities.
Define domain-oriented data product patterns including how shared/enterprise datasets are curated and reused.
Establish architectural boundaries and integration guidance for shared datasets vs. product-owned datasets.
Data Modeling Standards (Conceptual / Logical / Physical)
Define and enforce data modeling standards and templates appropriate to Fabric Lakehouse/Warehouse patterns and product analytics needs.
Provide modeling guidance for high-variance data types (telemetry, geospatial, documents) and hybrid operational-analytics use cases.
Define standards for schema evolution, versioning, and contract-first data interfaces (where applicable).
Governance, Security, and Compliance by Design
Design and implement a governance model covering classification, retention, lineage, and auditability.
Ensure compliance guardrails are built into delivery patterns and operational processes to meet GDPR, ISO 27001, and data residency requirements.
Define and enforce Fabric access controls using Entra ID, RBAC, and workspace-level controls (including guidance for separation of duties and least privilege).
CI/CD + Infrastructure as Code (IaC) for Fabric
Define and implement a CI/CD approach for Fabric artifacts as the enterprise source of truth.
Establish release patterns for Fabric changes (promotion strategy, environment separation, approvals, and quality gates) aligned to platform standards.
Manage Fabric-related platform configuration using Terraform as the IaC approach (including reusable modules/patterns).
Create golden path templates and guidance that product teams can adopt with minimal friction.
Capacity Planning, Cost Model, and Chargeback/Show back
Design Fabric capacity strategy (SKU sizing, workload isolation, scaling model) to support multiple products reliably.
Define guardrails and operational practices that reduce waste and improve predictability.
Reliability, Observability, and Operational Readiness
Define reliability patterns and operational standards for data pipelines and real-time workloads.
Integrate logging/monitoring with Log Analytics and security monitoring with Sentinel, including alerting and incident response considerations.
Define and operationalize data quality SLAs (freshness, completeness, accuracy, timeliness) and embed quality checks into delivery pipelines.
Consulting Engagement + Governance Forums
Participate in architectural governance and provide architecture review/sign-off, with authority to mandate standards when necessary to protect platform integrity
Partner closely with platform engineering to align patterns across identity, network, DevOps, and security.
Working Style & Mindset
Hands-on architect: you can design and build the critical Fabric artifacts to prove patterns.
Platform-oriented: you think in reusable standards, templates, and repeatable governance.
Strong consultative presence: you can advise product teams while also driving decisions and outcomes.
Comfortable with authority: you can mandate standards when required to protect the platform and business.
Documentation discipline: you produce clear ADRs, standards, and operating playbooks.
Engagement & Collaboration
Supports product teams through office hours and project-based sprints.
Works primarily with: Azure lead architect, security architect/engineer, DevOps platform engineer, and product engineering teams.
Data ownership remains with product teams; this role defines the how (standards/patterns/governance), not centralized ownership.
Requirements
Required Deliverables:
You will be accountable for producing the following:
Target-state architecture
Data model standards: conceptual / logical / physical + templates
Domain-oriented data product patterns and operating guidance
