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Job Title: Snowflake Data Architect
Location: Jersey City, NJ (Onsite)
Snowflake Data Architect to drive the design and evolution of large-scale, enterprise data platforms in a data-intensive environment. This role requires a strategic thinker and hands-on architect who can solve complex, real-world data challenges, optimize for cost and performance, and design future-ready data ecosystems.
The ideal candidate will have deep expertise in Snowflake architecture, strong experience with financial domain data, and a proven ability to design scalable, efficient, and resilient data solutions. This individual will play a key role in shaping enterprise data strategy, enabling advanced analytics, and modernizing legacy systems.
Required Skills & Qualifications
10 years of experience in Data Engineering / Data Architecture, with a strong focus on enterprise-scale systems.
Extensive hands-on expertise in Snowflake architecture and implementation.
Strong experience in financial services domain (banking, markets, asset management, etc.).
Proven experience designing cost-optimized, high-performance cloud data platforms.
Deep understanding of:
Data architecture principles
Data modeling (conceptual, logical, physical)
Data warehousing and lakehouse patterns
Experience with Data Development Platforms (DDP) and engineering frameworks.
Strong expertise in SQL and data performance tuning.
Experience designing and automating data marts and analytical layers.
Familiarity with modern data engineering tools (e.g., dbt, orchestration tools, etc.).
Strong understanding of scalability, governance, and security in enterprise data systems.
Ability to operate at both strategic and hands-on technical levels.
Key Responsibilities
Architecture & Design
Lead the design and development of scalable, secure, and high-performing Snowflake-based data architectures.
Define and implement enterprise-wide data models, data standards, and best practices.
Architect modern data platforms supporting high-volume, high-velocity financial data.
Design future-ready data ecosystems, ensuring scalability, flexibility, and long-term sustainability.
Problem Solving & Optimization
Solve complex, large-scale data challenges in a high-volume enterprise environment.
Design cost-effective Snowflake solutions, optimizing compute, storage, and data processing.
Implement performance tuning strategies for large datasets and complex workloads.
Drive architectural decisions balancing cost, performance, and data accessibility.
Data Platform & Engineering Enablement
Build and standardize Data Development Platforms (DDP) to accelerate engineering productivity.
Design frameworks for automating data pipelines and data marts at scale.
Enable self-service data capabilities while maintaining governance and control.
Modernization & Transformation
Lead initiatives to modernize legacy data platforms and transition to cloud-native architectures.
Design approaches for data migration, transformation, and integration across systems.
Establish reusable patterns and frameworks for enterprise-wide adoption.
Leadership & Strategy
Act as a trusted advisor to leadership on data architecture and platform strategy.
Proactively identify opportunities and recommend innovative solutions.
Mentor data engineers and guide teams on best practices, architecture patterns, and design principles.
Collaborate with multiple stakeholders including engineering, analytics, governance, and business teams.
Innovation & Automation
Leverage modern tools, automation frameworks, and AI-driven agents to enhance productivity and solution efficiency.
Continuously evaluate new technologies and tools to improve the data ecosystem.
Job Title: Snowflake Data Architect
Location: Jersey City, NJ (Onsite)
Snowflake Data Architect to drive the design and evolution of large-scale, enterprise data platforms in a data-intensive environment. This role requires a strategic thinker and hands-on architect who can solve complex, real-world data challenges, optimize for cost and performance, and design future-ready data ecosystems.
The ideal candidate will have deep expertise in Snowflake architecture, strong experience with financial domain data, and a proven ability to design scalable, efficient, and resilient data solutions. This individual will play a key role in shaping enterprise data strategy, enabling advanced analytics, and modernizing legacy systems.
Required Skills & Qualifications
10 years of experience in Data Engineering / Data Architecture, with a strong focus on enterprise-scale systems.
Extensive hands-on expertise in Snowflake architecture and implementation.
Strong experience in financial services domain (banking, markets, asset management, etc.).
Proven experience designing cost-optimized, high-performance cloud data platforms.
Deep understanding of:
Data architecture principles
Data modeling (conceptual, logical, physical)
Data warehousing and lakehouse patterns
Experience with Data Development Platforms (DDP) and engineering frameworks.
Strong expertise in SQL and data performance tuning.
Experience designing and automating data marts and analytical layers.
Familiarity with modern data engineering tools (e.g., dbt, orchestration tools, etc.).
Strong understanding of scalability, governance, and security in enterprise data systems.
Ability to operate at both strategic and hands-on technical levels.
Key Responsibilities
Architecture & Design
Lead the design and development of scalable, secure, and high-performing Snowflake-based data architectures.
Define and implement enterprise-wide data models, data standards, and best practices.
Architect modern data platforms supporting high-volume, high-velocity financial data.
Design future-ready data ecosystems, ensuring scalability, flexibility, and long-term sustainability.
Problem Solving & Optimization
Solve complex, large-scale data challenges in a high-volume enterprise environment.
Design cost-effective Snowflake solutions, optimizing compute, storage, and data processing.
Implement performance tuning strategies for large datasets and complex workloads.
Drive architectural decisions balancing cost, performance, and data accessibility.
Data Platform & Engineering Enablement
Build and standardize Data Development Platforms (DDP) to accelerate engineering productivity.
Design frameworks for automating data pipelines and data marts at scale.
Enable self-service data capabilities while maintaining governance and control.
Modernization & Transformation
Lead initiatives to modernize legacy data platforms and transition to cloud-native architectures.
Design approaches for data migration, transformation, and integration across systems.
Establish reusable patterns and frameworks for enterprise-wide adoption.
Leadership & Strategy
Act as a trusted advisor to leadership on data architecture and platform strategy.
Proactively identify opportunities and recommend innovative solutions.
Mentor data engineers and guide teams on best practices, architecture patterns, and design principles.
Collaborate with multiple stakeholders including engineering, analytics, governance, and business teams.
Innovation & Automation
Leverage modern tools, automation frameworks, and AI-driven agents to enhance productivity and solution efficiency.
Continuously evaluate new technologies and tools to improve the data ecosystem.