Consulting Senior Data Architect (Microsoft Fabric Focus)

Omm IT Solutions•Montreal, QC

86d•Remote

About The Position

We are seeking a Consulting Senior Data Architect with deep, production-grade Microsoft Fabric experience to accelerate our active rollout of Fabric for customer-facing and revenue-impacting digital products hosted in Azure. This is a hands-on role responsible for designing and building Fabric artifacts (pipelines, lakehouses, eventstream/real-time patterns), defining data architecture standards (conceptual/logical/physical), and implementing governance and security guardrails that scale across multiple product teams.

Requirements

10+ years in data architecture/data engineering roles, including platform-scale design.
Proven Microsoft Fabric production implementations you have delivered Fabric solutions that run in production with real operational constraints.
Deep hands-on expertise in Fabric areas central to our rollout: Data Engineering, Data Factory (pipelines), Real-time analytics / Event Streams.
Strong architecture capability across mixed data types: transactional, telemetry/events, documents, geospatial, time series.
Demonstrated experience implementing and governing: Data modeling standards (conceptual/logical/physical), Data governance (classification, retention, lineage), Security patterns using Entra ID, RBAC, workspace-level controls, Compliance guardrails for GDPR, ISO 27001, and data residency.
Strong DevOps fluency: CI/CD patterns and operational delivery, Terraform as mandatory IaC for repeatability and standardization.
Ability to define standards and enforce guardrails while maintaining a delivery-first, pragmatic approach.

Nice To Haves

Experience supporting IoT and telemetry-heavy product ecosystems.
Experience designing data quality frameworks and SLAs for operational analytics and near-real-time processing.
Familiarity integrating observability/security signals into Log Analytics and Sentinel.

Responsibilities

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).
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.
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).
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).
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.
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.
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.
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.
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.