Data Architect (AWS, Snowflake)

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.