Sr. Snowflake Architect

Location: City of Edinburgh

Job Title:

Sr. Snowflake Architect

Job Location:

Edinburgh/ Hybrid (2 days a week)
Is it Permanent /

Contract:

Open for Both (If contract initial 6 months)

Key Responsibilities Define the enterprise data strategy, reference architectures, and target-state blueprints anchored on the Snowflake AI Data Cloud, spanning data ingestion, storage, processing, serving, and governance.

Lead end-to-end modernization and migration programs to Snowflake including legacy EDW estates (e.g., Teradata, SQL Server), data lakes/lake houses, and integration platforms covering migration assessment, roadmap, cutover, parallel-run validation, and decommission plans.

Govern data modeling standards and patterns (dimensional star/snowflake, Data Vault, 3NF, wide-table designs), semantic layers (Snowflake semantic views, Horizon Context, BI semantic models), and data contracts for analytical and operational domains.

Establish ELT/ETL and streaming architectures on Snowflake Snowpipe, Snowpipe Streaming, Dynamic Tables, Streams and Tasks, external tables, and Apache Iceberg tables along with orchestration standards (dbt, Airflow, Snowflake Tasks) and CI/CD/Data Ops best practices.

Implement security-by-design in Snowflake: RBAC, tag-based governance (ABAC), dynamic data masking, row/column-level policies, Tri-Secret/external key management, network policies, Private Link, SSO/OAuth/MFA, audit trails, and access history.

Embed data quality, observability, and lineage using Snowflake Horizon Catalog and native observability features; define SLAs/SLOs, reconciliation frameworks, and incident/RCAs for data reliability.

Architect cross-region and cross-cloud data sharing, replication, and failover strategies (BC/DR) leveraging secure shares, Snowflake Marketplace/exchange, clean rooms, zero-copy cloning, Time Travel, and Fail-safe.

Drive Snowflake cost optimization and Fin Ops practices virtual warehouse sizing and scaling policies, auto-suspend/resume, resource monitors, and query performance tuning (clustering keys, Search Optimization, materialized views, result caching, adaptive compute).Enable AI- and GenAI-ready architectures on Snowflake Cortex AI, agentic governance, and semantic layers for AI agents ensuring a governed, AI-ready data foundation.

Partner with business, product, analytics, and engineering leaders to prioritize roadmaps and translate requirements into scalable Snowflake architectures.

Produce architecture artifacts (logical/physical models, ADRs, data flow diagrams, runbooks) and ensure governance alignment with enterprise standards.

Mentor and lead engineers and data modelers; conduct design and code reviews; elevate engineering practices across teams.

Required Skills & Experience
20+ years in data architecture and engineering, including 8-10+ years in lead/enterprise architect roles delivering large-scale data platforms, with at least 5+ years of hands-on Snowflake architecture experience.

Proven track record architecting and delivering enterprise data warehousing and lakehouse solutions on Snowflake, including migrations from legacy EDW (Teradata, Oracle, SQL Server, etc.) and modern platforms (Big Query, Redshift, Synapse, etc.).Deep data modeling expertise (dimensional, Data Vault, 3NF) and semantic design, including Snowflake semantic views and Horizon Context.

Strong ELT/ETL and orchestration background Snowflake-native pipelines (Snowpipe, Dynamic Tables, Streams, Tasks), dbt (including dbt Projects on Snowflake), Airflow, Azure Data Factory, Informatica, or similar.

Hands-on experience with major cloud platforms (AWS, Azure, or GCP) including Snowflake deployment, networking (Private Link), and cloud storage integration (S3, ADLS, GCS); familiarity with lakehouse stacks (Databricks, Delta Lake, Apache Iceberg) is a plus.

Mastery of Snowflake security and governance: RBAC, tag-based governance, dynamic data masking, row access policies, column-level security, Tri-Secret encryption, network policies, Horizon Catalog, access history, and data classification.

Streaming and CDC patterns (Kafka, Kinesis, Snowpipe Streaming) and event-driven architectures for near real-time use cases.

Data governance and metadata management (catalogs, lineage, MDM, data quality frameworks) with familiarity in regulatory regimes (e.g., GDPR/CCPA/HIPAA/PCI-DSS/SOX).Proficiency in SQL and at least one programming language for data engineering (Python preferred), plus CI/CD and IaC (Git, pipelines, Terraform/Cloud Formation) including declarative, version-controlled…