Senior Data Architect / Consultant (Snowflake or Databricks)

Role overview

Advise enterprise clients on modernizing data platforms and lead architecture and delivery across multiple long-term engagements. This client-facing role combines strategic guidance for business and technology leaders with hands-on technical judgment, helping teams select appropriate warehouse or lakehouse approaches, migrate data reliably, and build governed platforms that support analytics and future AI use cases.

Responsibilities

- Lead discovery with business and technology stakeholders, clarify priorities, and explain architecture choices, risks, and investment trade-offs. - Assess existing data estates and design target architectures, integration patterns, migration phases, and operating arrangements. - Guide batch and incremental ingestion, ETL/ELT, change data capture, orchestration, reconciliation, testing, and production cutover. - Review and troubleshoot SQL, pipelines, data models, and integrations, addressing reliability, performance, and cost during delivery. - Design governed data layers with appropriate modeling, quality checks, lineage, security, and lifecycle controls. - Plan roadmaps, dependencies, architecture decisions, and stakeholder updates; develop secure data-sharing approaches and potential data products.

Requirements

- Senior, client-facing data architecture or consulting experience with production expertise in either Snowflake or Databricks. - Ability to work from strategic discovery through technical delivery and communicate clearly with senior stakeholders and engineering teams. - Experience modernizing enterprise data environments and delivering migrations and integrations into production. - Practical judgment across data modeling, pipeline reliability, performance, cost, governance, and security. - Strong ownership of plans, risks, dependencies, and stakeholder communication, with the ability to adapt to different client settings.

Nice to have

Experience with medallion-style data layers, cross-organization data sharing, private exchanges or marketplaces, and data products. The work may also involve identifying opportunities in analytics and AI.