Data Architect Snowflake( Need Local to VA only, F2F Interview)

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ROLE IS 1 WEEK ON-SITE, 1 WEEK REMOTE, ONLY WILL CONSIDER CANDIDATES LOCAL TO RICHMOND, VA DAY 1 AS IN PERSON INTERVIEW NEEDED.

Need LinkedIn and local DL

Job Description-

The IT Architect Data & Snowflake is a strategic technical leader within the Enterprise Data & Analytics team, responsible for defining the architecture, standards, best practices and future direction of the enterprise data platform. This role designs scalable data pipelines, governs Snowflake usage across the organization, and develops high quality data models that power analytics, BI, AI/ML, and data driven decision making.

The architect ensures that the data ecosystem is secure, governed, cost optimized, and capable of supporting enterprise self service analytics, operational reporting, and advanced analytics initiatives.

We are seeking a hands on Data Architect with a strong ETL and Data Engineering background to design, build, and optimize large scale data ingestion, transformation, and delivery platforms. This role is focused on creating reliable, observable, and high performance data pipelines that support enterprise analytics, reporting, and advanced AI/ML use cases.

You will architect end to end data engineering solutions across on prem and cloud platforms, lead data integration strategy, and establish best practices around data quality, monitoring, and operational excellence.

Key Responsibilities-

Technical Leadership

• Serve as the technical authority for ETL and data engineering architecture.
• Review pipeline designs, code standards, and performance benchmarks.
• Mentor and guide data engineers on best practices and modern data engineering patterns.
• Partner with stakeholders to translate business data requirements into scalable technical solutions.
• Ability to lead complex data engineering initiatives in enterprise environments
  
  

ETL / ELT Architecture & Design

• Design and own end to end ETL/ELT architectures for batch and incremental data processing.
• Define patterns for data ingestion from Oracle Exadata and other source systems into cloud platforms.
• Architect high volume, high throughput pipelines supporting structured and semi structured data.
• Continuously evaluate and improve data infrastructure and workflows
  
  

Establish standards for:

• Data transformation logic
• Schema evolution
• Error handling and reprocessing
• Performance tuning
  
  

Define best practices for:

• Metadata management
• Parameterization and reusability
• Pipeline deployment and versioning
  
  

Strong expertise in:

• Batch and incremental data processing
• ELT patterns and push down processing
• Performance tuning of large ETL workloads
  
  

Enterprise Data Architecture & Design

• Architect end to end data pipelines (batch, streaming, real time) to support enterprise analytics, BI, and data products.
• Define architectural standards, including database design, warehouse sizing, multi-cluster strategies, RBAC, and performance optimization.
• Create and maintain enterprise semantic models, curated datasets, and reusable data assets to enable self-service analytics.
• Develop architecture patterns for AI/ML feature pipelines, analytical sandboxes, and model scoring in Snowflake.
  
  

Governance, Standards & Best Practices

• Establish data engineering, modeling, and transformation standards across the analytics ecosystem (e.g., naming conventions, ELT frameworks, versioning).
• Implement DevOps/DataOps practices CI/CD pipelines, automated testing, data quality checks, and observability across data pipelines.
• Partner with Data Governance to define standards for data quality, lineage, metadata management, and cataloging.
• Implement Snowflake security, access controls, auditing, and cost management practices.
  
  

Analytics & Data Product Enablement

• Collaborate with BI and analytics teams to design scalable, governed data models supporting dashboards, KPIs, and advanced analytics.
• Architect data solutions that support predictive analytics, forecasting, segmentation, and personalization use cases.
• Guide development of data products, ensuring they follow enterprise standards and meet business requirements.
  
  

Cross-Functional Collaboration

• Partner with data engineering, analytics, business SMEs, cloud infrastructure, and cybersecurity teams to design reliable and secure data architectures.
• Translate business needs into scalable technical solutions that support enterprise reporting, analytics, and insight generation.
• Participate in Architecture Review Boards, solution design sessions, and enterprise data strategy planning.
  
  

Operational Excellence

• Establish standards for monitoring, alerting, SLA/SLO management, and operational resiliency of analytics pipelines.
• Ensure data solutions meet enterprise requirements for reliability, performance, scalability, and disaster recovery.
• Lead root cause analysis for data platform issues and drive remediation of architectural gaps.
• Proven experience with data quality, data observability, and pipeline monitoring