Senior Group Technical Architect

Job Description: Databricks & Snowflake Data Architect

Role Overview

We are looking for an experienced Databricks & Snowflake Data Architect to design and implement scalable, secure, and high-performance enterprise data platforms. The architect will define end-to-end data architecture across Databricks and Snowflake, covering data ingestion, transformation, storage, governance, integration, and performance optimization.

Design end-to-end cloud data architecture using Databricks and Snowflake.Define scalable Lakehouse, Data Lake, Data Warehouse, and Data Mart architectures based on business and technical requirements.Architect data ingestion and transformation pipelines for structured, semi-structured, batch, and streaming workloads.Design Databricks solutions leveraging Delta Lake, Unity Catalog, Spark, notebooks, workflows, and medallion architecture.Design Snowflake architecture covering databases, schemas, virtual warehouses, storage, data sharing, security, and workload management.Define patterns for integrating Databricks and Snowflake within the enterprise data ecosystem.Drive data modeling approaches including dimensional, normalized, and analytical models.Establish architectural standards for security, access control, data governance, metadata management, lineage, and data quality.Optimize Spark and SQL workloads for performance, scalability, reliability, and cost efficiency.Design integration patterns with cloud storage, APIs, databases, ETL/ELT platforms, BI tools, and downstream applications.Define reusable architecture frameworks, design patterns, coding standards, and technical best practices.Lead architecture reviews and provide technical guidance to engineering teams.Work closely with business stakeholders, data engineers, analysts, product teams, and cloud/platform teams.Support migration and modernization of legacy data warehouse/data lake platforms to Databricks and Snowflake.Evaluate technical requirements and make appropriate architecture decisions considering scalability, security, performance, maintainability, and cost.

Databricks

• Strong hands-on architecture experience with Databricks
• Apache Spark / PySpark
• Spark SQL / SQL
• Delta Lake
• Unity Catalog
• Databricks Workflows
• Lakehouse architecture
• Medallion Architecture: Bronze, Silver, Gold
• Performance optimization and cluster/workload tuning
• Data governance and security

Snowflake

• Strong Snowflake architecture and implementation experience
• Snowflake database and schema design
• Virtual Warehouses
• Snowflake SQL
• Data loading and ELT patterns
• Performance and query optimization
• Role-Based Access Control (RBAC)
• Secure data sharing
• Time Travel / cloning concepts
• Cost and workload optimization

Data Architecture

• Enterprise Data Architecture
• Data Lake / Lakehouse / Data Warehouse architecture
• ETL/ELT architecture
• Data modeling
• Batch and real-time/streaming architecture
• Data governance, lineage, metadata, and data quality
• Cloud-native data architecture
• CI/CD and DevOps practices for data platforms

Cloud & Integration Skills

Strong experience with at least one major cloud platform:

• Azure: ADLS Gen2, Azure Data Factory, Key Vault, Entra ID and related Azure data services
• AWS: S3, IAM, Glue and related AWS data services
• GCP: Cloud Storage, IAM and related Google Cloud data services

Experience integrating Databricks/Snowflake with tools such as Power BI, Tableau, dbt, Kafka, Airflow, Azure Data Factory, or equivalent technologies is desirable.

Architecture Expectations

The candidate should be capable of owning architecture decisions across:

Source Systems → Ingestion → Databricks/Lakehouse → Transformation → Snowflake → Semantic/Serving Layer → BI & Analytics

The role should also understand when workloads should be processed or served through Databricks versus Snowflake, rather than simply implementing both platforms independently.

Required Experience

• Strong overall experience in Data Engineering/Data Architecture.
• Significant architecture and solution-design experience with Databricks and Snowflake.
• Proven experience designing enterprise-scale cloud data platforms.
• Experience handling complex data migration and modernization programs.
• Strong understanding of security, governance, performance tuning, and cost optimization.
• Ability to translate business requirements into scalable technical architecture.
• Strong stakeholder management and technical leadership skills.

Databricks Certified Data Engineer Professional / relevant Databricks certificationSnowPro Core / SnowPro Advanced ArchitectAzure, AWS, or Google Cloud architecture/data certifications

 

A strong candidate should be more than a Databricks or Snowflake developer. They should demonstrate hands-on architecture ownership.