Snowflake Architect

SoftNice UG; City of Edinburgh; On-site; GBP 90,000 - 130,000; Full time

Job summary

SoftNice UG is seeking an experienced Data Architect to lead the enterprise Snowflake strategy and modernization program. You will define target-state blueprints, drive migrations from legacy systems, and govern data models, security, and governance across the data platform.

You will design ELT/ETL and streaming architectures, ensure cost-efficient Snowflake operations, and enable AI-ready data foundations. Collaboration with business, product, and engineering leaders is essential.

Qualifications

• 20+ years in data architecture and engineering.
• 8-10+ years in lead/enterprise architect roles.
• 5+ years hands-on Snowflake architecture experience.
• Proven track record delivering enterprise data warehousing/lakehouse on Snowflake.
• Deep data modeling and semantic design expertise.
• Strong ELT/ETL and orchestration background.
• Hands-on with AWS/Azure/GCP and Snowflake deployment.
• Mastery of Snowflake security and governance.
• Streaming/CDC patterns and event-driven architectures.
• Data governance, metadata management, regulatory awareness.
• SQL and Python proficiency; CI/CD and IaC.
• Ability to assess legacy estates and plan migration roadmaps.
• Excellent communication and stakeholder management.
• Snowflake certifications preferred.

Responsibilities

• Define the enterprise data strategy and target-state blueprints anchored on Snowflake AI Data Cloud.
• Lead modernization and migration programs to Snowflake, including legacy EDW and data lakes.
• Govern data modeling standards and semantic layers; define data contracts.
• Establish ELT/ETL and streaming architectures with Snowflake pipelines and orchestration.
• Implement security-by-design with RBAC, masking, network policies, and MFA.
• Embed data quality, observability, and lineage; define SLAs/SLOs and RCAs.
• Architect cross-region/cross-cloud data sharing with secure shares and time travel.
• Drive Snowflake cost optimization and FinOps practices.
• Enable AI-ready architectures for Snowflake-powered AI agents.
• Partner with business/analytics/engineering to translate roadmaps.
• Produce architecture artifacts and ensure governance alignment.
• Mentor engineers and conduct design/code reviews.

Skills

Snowflake architecture

Enterprise data architecture

Data modeling (dimensional, Data Vault

ETL/ELT orchestration

Cloud platforms (AWS/Azure/GCP)

Data governance & security

Streaming & CDC patterns

SQL & Python

CI/CD & IaC

Snowflake certifications

Migration & modernization

Stakeholder management

Tools

Snowflake

dbt

Airflow

Terraform/CloudFormation

Job description

• 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/lakehouses, 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/DataOps 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 FinOps 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 (BigQuery, 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/CloudFormation) — including declarative, version-controlled Snowflake object management.
• Proven ability to assess legacy estates, create pragmatic Snowflake migration roadmaps, and deliver phased outcomes with measurable business value.
• Excellent communication and stakeholder management; ability to influence architecture decisions and align cross-functional teams.
• Snowflake certifications (SnowPro Core, SnowPro Advanced: Architect) are strongly preferred