Azure Data Architect

Azure Data Architect
Brampton - Hybrid (2-3 days from Office)

Experience Range: 8+ years

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.

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.

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.

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 ), 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.

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 Fin Ops for data platforms: capacity planning, cost governance, tagging, budgets/alerts, and rightsizing.

Dev Ops, CI/CD & Observability

Establish CI/CD for data and infrastructure using Azure Dev Ops or Git Hub 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.

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:

Azure Synapse Analytics / Azure SQL

Microsoft Fabric (Lakehouse, Warehouse, Semantic Models / DAX)

Databricks (Spark, Delta Lake, Unity Catalog), PySpark/Spark SQL

ADLS Gen2, Data Factory / Synapse Pipelines / Fabric Data Factory

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 & Dev Ops:
Terraform/Bicep, Git, Azure Dev Ops/Git Hub, 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).

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