Data Analytics Solutions Architect

Job Title: Data & Analytics Solutions Architect (AWS + Snowflake + Cortex)

Location: NJ/NYC, Columbus OH, Washington DC area, Charlotte, NC, Milwaukee, WI
Full-Time

Must Have Technical/Functional Skills:

14+ years of experience in Enterprise Data Architecture, AWS Data Platforms, Snowflake, Data Engineering, and Cloud Modernization.

Proven experience in Enterprise Solution architecture using AWS, Snowflake, Cortex and related technology components across the Data to Insights end to end value chain.

Strong hands-on expertise in AWS Data Services, Snowflake, Snowflake Cortex, Snowflake Horizon Catalog, Apache Iceberg / Snowflake Open Catalog, Python, PySpark, and SQL.

Proven experience designing and implementing Lakehouse Architectures, enterprise data platforms, and modern analytics solutions on AWS.

Strong understanding of the Snowflake Data Cloud, including Snowflake SQL, Workflows, Snowpipe, Snowflake Model Registry, Snowflake Horizon Catalog, Serverless Compute, and Performance Optimization.

Experience building AI-enabled analytics solutions using Snowflake Cortex, semantic models, natural language analytics, and governed data access frameworks.

Expertise in Data Engineering including ETL/ELT, batch and streaming integration, APIs, orchestration, data quality, metadata management, lineage, observability, and DataOps practices.

Strong knowledge of Enterprise Data Architecture, including data models, data products, lakehouse and warehouse patterns, semantic layers, and master/reference data management.

Experience implementing Security, Governance, and Compliance controls including IAM/RBAC, encryption, privacy, masking, auditability, retention, and responsible AI practices.

Proven experience leading architecture definition, HLD/LLD preparation, NFR assessment, migration planning, technical governance, and production readiness activities.

Experience supporting cloud transformation initiatives, solutioning, RFP responses, effort estimation, and client-facing architecture discussions.

Good understanding of BFSI regulatory, security, privacy, resilience, and audit requirements.

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Management, or a related discipline.

Preferred certifications in AWS Solutions Architecture, Snowflake, Data Engineering, Enterprise Architecture, Security, or AI/ML.

Roles & Responsibilities:

Engage with business and technology stakeholders to understand data, analytics, AI, and cloud transformation requirements.

Define end-to-end architecture for AWS Snowflake-based data, analytics, and AI platforms.

Design scalable, secure, and high-performance Lakehouse solutions using AWS Data Services, Snowflake, Apache Iceberg / Snowflake Open Catalog, and Snowfl ake Horizon Catalog.

Architect data ingestion, integration, batch, streaming, and data engineering solutions supporting enterprise analytics and AI use cases.

Design and implement AI-enabled analytics solutions using Snowflake Cortex, semantic models, and governed data access frameworks.

Establish data governance, security, metadata, lineage, observability, and responsible AI standards across the platform.

Lead data platform modernization, migration, and cloud transformation initiatives on AWS.

Define architecture standards, reusable frameworks, best practices, and DataOps delivery models.

Provide technical leadership across solution design, implementation, testing, migration, deployment, and production support activities.

Conduct architecture reviews, identify risks and dependencies, and guide teams on mitigation strategies and best practices.

Drive platform performance optimization, reliability improvements, and cost optimization across AWS and Snowflake environments.

Collaborate with data engineers, AI specialists, architects, platform teams, and client stakeholders to deliver enterprise-scale solutions.

Support solutioning, RFP responses, effort estimation, technical presentations, and client workshops.

Mentor architects and engineers and contribute to capability development, technical assessments, and knowledge-sharing initiatives.

Develop reusable accelerators, reference architectures, implementation frameworks, and best practices for AWS Snowflake and AI solutions.