Data Architect (AWS, Snowflake)

DigiEx Group; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

DigiEx Group seeks a senior data leader to own data architecture and drive technical discovery for client engagements. You will guide data engineers, shape platform direction, and set delivery standards across teams.

Lead decisions on modeling, governance, security, and auditability while mentoring engineers and shaping the data engineering practice. Strong AWS/Snowflake and lakehouse experience is required.

Qualifications

• Bachelor’s degree or higher in computer science, information technology, data engineering, data architecture, or related field.
• 7+ years in data engineering or architecture with 3+ years in senior/lead/architect role.
• Hands-on AWS and Snowflake architecture, performance, scalability and cost optimization.
• Lakehouse experience with Delta Lake, Iceberg, or Hudi.
• IaC tools such as Terraform or Pulumi; governance and security controls.
• Experience with orchestration/transformation tools like Airflow or dbt.

Responsibilities

• Own data architecture and technical discovery for client engagements, guiding platform direction and delivery standards.
• Define platform architecture, technology choices, specs, and actionable roadmaps.
• Set governance, security, access control, lineage, and auditability standards.
• Advise senior data engineers on complex decisions and scalability risks.
• Establish engineering practices and hiring criteria to strengthen data capabilities.
• Review AI-assisted code for correctness, security, and performance.

Skills

Data architecture

Leadership

AWS

Snowflake

Delta Lake

Iceberg

Hudi

Terraform

Pulumi

Airflow

dbt

Data governance

Client leadership

Education

Bachelor’s degree or higher in CS/IT/Data

Tools

Terraform

Pulumi

Airflow

dbt

Job description

Role overview

Own data architecture and technical discovery for client engagements, from assessing existing environments to setting platform direction and delivery standards. This is a senior, hands-on leadership role: you will guide data engineers, make architecture decisions, and help build the engineering practice while supporting secure, scalable data platforms.

Responsibilities

• Assess client data environments and define platform architecture, technology choices, technical specifications, and actionable roadmaps.
• Select and standardize data modeling approaches, documenting decisions, alternatives, and trade-offs.
• Set standards for governance, security, compliance, access control, lineage, sensitive data protection, and auditability.
• Advise senior data engineers on complex technical decisions and identify risks to scalability, integration, performance, and observability.
• Establish engineering practices and hiring criteria to strengthen data engineering capabilities.
• Review AI-assisted code for correctness, security, performance, and maintainability.

Requirements

• Bachelor’s degree or higher in computer science, information technology, data engineering, data architecture, or a related area; equivalent advanced expertise is preferred.
• At least seven years in data engineering or architecture, including three or more years in a senior, lead, or architect role with platform-level decision ownership.
• Strong hands-on engineering skills, including the ability to contribute at senior data engineer level.
• Experience assessing existing data systems and producing a practical baseline, roadmap, or modernization plan.
• Hands-on AWS and Snowflake architecture experience, including performance, scalability, and cost optimization; plus lakehouse experience with Delta Lake, Iceberg, or Hudi.
• Familiarity with infrastructure-as-code tools such as Terraform or Pulumi, governance and security controls, and orchestration or transformation tools such as Airflow or dbt.
• Business-level English and confidence leading architecture discussions with client technical and business stakeholders.

Nice to have

• Multi-cloud or hybrid-cloud delivery, including Azure or Google Cloud.
• Data infrastructure for machine learning, such as feature stores, model registries, and data versioning.
• Experience owning both architecture and delivery, or with Kafka, Spark, PySpark, or Databricks.