Azure Data Architect
Brampton - Hybrid (2-3 days from Office)
Experience Range : 8+ years
1) Architecture & Solution Design
- Define end to end data platform reference architectures using Azure services such as Azure Synapse / SQL, Microsoft Fabric (Lakehouse, Warehouse, Semantic Models), Databricks, Azure Data Lake Storage Gen2, Data Factory / Synapse Pipelines, Event Hubs / Kafka, and Stream Analytics.
- Select appropriate storage and compute patterns (e.g., lakehouse, data mesh, medallion zones, Delta format) aligned to use cases, SLAs, and scalability needs.
- Produce and maintain HLD / LLD, Architecture Decision Records (ADRs), ERDs, and interface / API specifications.
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2) Data Modeling & Engineering Patterns
Establish dimensional (Kimball), 3NF, and Data Vault models where appropriate; promote semantic models for BI (Power BI / Fabric).Define ingestion and processing patterns (batch, streaming, CDC, micro batch) and file format standards (Parquet, Delta Lake, Avro).Set coding standards and performance guidelines for SQL, T SQL, PySpark, and Spark SQL.________________________________________
3) Data Integration & Pipelines
Architect robust ETL / ELT pipelines with ADF / Synapse Pipelines, Fabric Data Factory, Databricks Workflows, and orchestration using ADF triggers and Logic Apps.Integrate data from ERPs / CRMs, SaaS applications, APIs, and on prem systems via Self hosted Integration Runtime, Event Hubs, Kafka, Service Bus, and Change Data Capture tools.________________________________________
4) Governance, Security & Compliance
Implement data governance (catalogs, lineage, glossary, ownership) using Microsoft Purview; establish data mesh guardrails and federated governance practices.Enforce security using Azure AD (Entra ID), RBAC / ABAC, Managed Identities (MSI), Key Vault, Private Endpoints, VNet integration, row / column level security, dynamic data masking, and encryption at rest / in transit.Define data quality, master / reference data practices, and support audit, retention, and regulatory requirements.________________________________________
5) Performance, Reliability & Cost Optimization
Design for scalability and resilience (partitioning, caching, Z ordering, concurrency control, workload isolation).Tune Spark and SQL workloads; optimize storage layouts; configure auto optimize / auto compaction strategies (e.g., Delta).Own FinOps for data platforms : capacity planning, cost governance, tagging, budgets / alerts, and rightsizing.________________________________________
6) DevOps, CI / CD & Observability
Establish CI / CD for data and infrastructure using Azure DevOps or GitHub Actions, with Terraform / Bicep and deployment strategies (blue green, canary).Implement monitoring and alerting with Azure Monitor, Log Analytics, Fabric / Databricks observability, and custom dashboards; define SLOs / SLIs for data pipelines.Define backup & disaster recovery strategies and RPO / RTO targets for critical datasets.________________________________________
7) Leadership & Collaboration
Lead data architecture governance and design reviews; mentor data engineers and analysts.Partner with Product, Security, Enterprise Architecture, and Business stakeholders to align roadmaps and priorities.Drive vendor / tool evaluations, proofs of concept, and build vs buy decisions.________________________________________
Skills & Experience
Required
7+ years in data architecture / engineering, including 3–5 years with Azure data services.Hands on expertise with several of the following :o Azure Synapse Analytics / Azure SQL
o Microsoft Fabric (Lakehouse, Warehouse, Semantic Models / DAX)
o Databricks (Spark, Delta Lake, Unity Catalog), PySpark / Spark SQL
o ADLS Gen2, Data Factory / Synapse Pipelines / Fabric Data Factory
o Event Hubs, Service Bus, Kafka, Stream Analytics, CDC tools
Strong data modeling (dimensional, 3NF, Data Vault) and ELT / ETL design.Proficient in SQL, T SQL, PySpark; solid understanding of metadata management and lineage.Security & networking basics : AAD, RBAC, VNets, Private Endpoints, Key Vault, Managed Identities.IaC & DevOps : Terraform / Bicep, Git, Azure DevOps / GitHub, automated testing for data pipelines.Preferred
Experience with data mesh and lakehouse patterns; MLOps handoffs (Feature Store, Model Registry).Performance engineering at large scale (TB–PB).Knowledge of data quality frameworks (e.g., Great Expectations, Deequ) and DQ scorecards.Exposure to Microsoft Purview (catalog, classifications, policies).Familiarity with privacy / compliance (SOX, HIPAA, GDPR, PCI).