Data Architect

Role Summary

We are seeking an experienced Data Architect to define and evolve enterprise data architecture and design scalable, secure, high-performing data solutions aligned with business strategy. The role will shape target-state architecture, platform capabilities, data patterns, and modernization roadmaps across ingestion, storage, transformation, governance, analytics, and AI consumption.

The ideal candidate combines deep architecture expertise with strong understanding of Finance, Sales, or Operations. This role partners with Product, Engineering, Analytics, AI, and business leaders to establish architecture direction, guide solution design, and ensure the data platform remains reusable, interoperable, governed, and future-ready.

Key Responsibilities

Design Scalable Data Solutions

• Architect end-to-end data solutions spanning ingestion, integration, transformation, storage, serving, analytics, and AI consumption.
• Define conceptual, logical, and physical data architectures and select appropriate patterns for batch, streaming, and near-real-time use cases.
• Ensure designs meet scalability, performance, resilience, availability, security, and disaster-recovery requirements.
• Lead architecture reviews and guide engineering teams through solution implementation.

Build and Evolve the Data Platform

• Define target-state architecture for modern data warehouses, lakehouses, data lakes, and analytical platforms.
• Create platform blueprints, reference architectures, reusable patterns, and technology standards.
• Guide legacy modernization and migration to cloud-native, Snowflake-aligned architectures.
• Evaluate platform capabilities and recommend investments based on business value, interoperability, scalability, and cost.

Drive Data Strategy and Collaboration

• Translate business strategy into data capabilities, architecture roadmaps, and prioritized modernization initiatives.
• Partner with Product, Engineering, Analytics, AI, Security, and business leaders on architecture decisions.
• Communicate trade-offs, risks, dependencies, and recommendations to technical and executive stakeholders.
• Provide architecture leadership and mentor teams on patterns and design decisions.

Ensure Standards and Innovation

• Establish standards for data modeling, integration, metadata, lineage, quality, interoperability, security, and responsible data use.
• Ensure solution designs align with enterprise architecture, privacy, compliance, and governance principles.
• Evaluate emerging technologies in AI, automation, streaming, and cloud data platforms.
• Promote reusable data products, engineering excellence, and continuous architecture improvement.

Required Skills & Experience

12+ years of experience in Data Engineering, Data Architecture, Data Warehousing, or Enterprise Architecture.

Proven experience defining and implementing enterprise-scale data architectures and cloud data platforms.

Experience leading modernization initiatives and architecture roadmaps across complex data ecosystems.

Strong record of designing solutions that support analytics, operational reporting, AI/ML, and business growth.

Mandatory Skills

Enterprise and solution data architecture

Snowflake-aligned platform architecture

Conceptual, logical, and physical data modeling

Data warehouse, lakehouse, and data-lake architecture

Batch, streaming, real-time, API, and event-driven integration patterns

Cloud architecture on Azure, AWS, or GCP

Metadata, lineage, data-quality, privacy, and governance architecture

Performance, resilience, availability, disaster recovery, and cost optimization

Architecture standards, reference patterns, design reviews, and technology evaluation

Functional / Domain Experience

Experience in at least one of the following domains:

• Finance — FP&A, revenue analysis, budgeting, forecasting, or P&L analytics
• Sales — sales operations, pipeline analytics, CRM insights, or revenue growth
• Operations — process optimization, workforce planning, productivity, or operational analytics

Executive stakeholder management

Technical leadership and strategic roadmap development

Ability to connect business strategy with target-state data capabilities and investment priorities

Technical Skills

Snowflake Data Cloud architecture and solution design

Enterprise data warehousing, lakehouse, data mesh, and data-product architectures

Dimensional modeling, Data Vault, and semantic data models

ELT/ETL, CDC, streaming, and data-pipeline architecture

Snowpark using Python or SQL and Snowflake Cortex AI

Performance engineering, workload management, and FinOps principles

Data sharing, Snowflake Marketplace, and Native Applications

GenAI, LLMs, RAG, vector search, and AI-ready platform architecture

APIs, event-driven architecture, microservices integration, CI/CD, infrastructure as code, and Git