Data Architecture
Location: Bellevue, WA (Onsite)
Mandatory Skills: ANSI-SQL, Azure Data Factory (ADF), Azure Purview, Azure Synapse Analytics, Microsoft Fabric.
Key Responsibilities:
Data Architecture & Modeling
- Architect end-to-end data flows across ingestion, storage, transformation, and consumption layers using Azure Synapse, Data Factory, Microsoft Fabric, Databricks, and ADLS Gen2.
- Design logical, physical, dimensional, Data Vault, and semantic data models for analytical, operational, and AI workloads.
- Build Lakehouse and Direct Lake models optimized for Power BI and ML pipelines.
- Develop reusable data architecture patterns, templates, and metadata-driven orchestration frameworks.
- Collaborate with AI/ML teams to enable GenAI-ready data models, feature extraction, embeddings, and contextual reasoning.
Performance Optimization & Scalability
- Optimize data pipelines, SQL queries, and workloads across Synapse, Databricks, Power BI, and Fabric.
- Implement partitioning, indexing, caching, and materialized views for large-scale datasets.
- Establish performance benchmarking and continuous monitoring frameworks.
- Design cost-efficient architectures using serverless compute, caching, and autoscaling.
- Apply AI-assisted anomaly detection to proactively identify performance issues and SLA breaches.
Data Governance, Security & Compliance
- Implement AI-driven data governance using Microsoft Purview for cataloging, classification, and lineage.
- Establish data quality frameworks, automated validation, and schema evolution processes.
- Enforce Zero Trust security, RBAC, RLS, encryption, masking, tokenization, and DLP policies.
- Integrate Microsoft Entra ID, Defender for Cloud, and Purview Data Policies.
- Support Responsible AI governance and regulatory compliance, including GDPR, SOC 2, and HIPAA.
CI/CD, DataOps & AI Enablement
- Establish CI/CD pipelines using Azure DevOps, GitHub Actions, and Fabric.
- Automate testing, deployments, environment provisioning, and pipeline monitoring.
- Implement DataOps standards and metadata-driven DevOps practices.
- Conduct architecture reviews, audit readiness assessments, and performance health checks.
- Collaborate with AI/ML teams on feature stores, MLOps, model lineage, GenAI automation, and AI observability.
Required Expertise:
- Hands-on experience with Azure Synapse, ADF, Microsoft Fabric, ADLS Gen2, and Microsoft Purview.
- Strong SQL, dimensional modeling, Data Vault, semantic modeling, and performance optimization expertise.
- Experience with CI/CD, DevOps automation, and metadata-driven frameworks.
- Understanding of AI principles and governance is preferred.