The Role
Design and own a Snowflake and AWS data platform supporting analytics, machine learning, and generative AI. Responsibilities include data architecture, modeling, batch and real-time pipelines, governance, security, privacy, performance and cost optimization, AI data preparation, stakeholder engagement, technical roadmapping, and reviewing data engineering work.
Summary Generated by Built In
Our Client is a fast-growing technology startup delivering data and AI solutions for a large international enterprise client. You'll work at the intersection of startup speed and enterprise scale: small team, short decision paths, and real ownership, applied to a data landscape spanning multiple countries, business units and systems.
As our Data Architect, you will design and own the data platform that powers analytics and AI use cases for our key client. You'll define how data is ingested, modelled, governed and served on Snowflake and Microsoft Azure. You'll also make sure the platform is ready for machine learning and generative AI workloads, not just reporting.
Responsibilities:
- Design and evolve the target data architecture on Snowflake and Microsoft Azure, covering ingestion, storage, transformation, and serving layers
- Define data models (dimensional, Data Vault or domain-oriented) that support both BI and AI/ML use cases
- Architect data pipelines for batch and near-real-time processing (e.g. Azure Data Factory, Azure Functions, Event Hubs, Snowpipe, dbt)
- Prepare the platform for AI workloads, including feature data, training datasets, vector/embedding storage, and RAG data pipelines (e.g. Snowflake Cortex, Azure OpenAI, Azure AI Search, Azure Machine Learning)
- Set standards for data quality, lineage, metadata, and governance, including Entra ID–based access control, masking, and GDPR compliance (e.g. Microsoft Purview)
- Optimise Snowflake performance and cost through warehouse sizing, clustering, and query tuning
- Work directly with client stakeholders, both business and IT, to translate requirements into architecture decisions and roadmaps
- Guide and review the work of data engineers, and document architecture decisions (ADRs, diagrams)
Requirements
- 8 years in data engineering or data architecture, including at least 3 years in an architect or technical lead role
- Hands-on production experience with Snowflake (data modelling, security, performance tuning, cost management)
- Strong knowledge of Azure data services (ADLS Gen2, Azure Data Factory, Entra ID, Key Vault, networking and private endpoints)
- Advanced SQL and working knowledge of Python
- Experience designing data platforms that support ML or AI use cases
- Understanding of data governance, security, and privacy requirements in enterprise environments
- Experience working with large, international organisations and multiple stakeholders
- Fluent English (C1); you'll work daily with an international client
Nice to have:
- dbt, Airflow/MWAA, Terraform or other IaC
- Snowflake Cortex, Snowpark, Azure OpenAI, or Azure Machine Learning
- Experience with GenAI architectures (RAG, vector databases, LLM integration)
- Databricks or Microsoft Fabric exposure (common alongside Snowflake in Azure estates)
- Snowflake SnowPro or Microsoft Azure certifications (e.g. Azure Solutions Architect Expert)
- Streaming (Kafka, Kinesis)
Benefits
- Real architectural ownership: you design the platform, not just maintain it
- Enterprise-scale data and AI challenges with startup flexibility
Skills Required
- 8 years of experience in data engineering or data architecture
- At least 3 years in an architect or technical lead role
- Hands-on production experience with Snowflake, including data modeling, security, performance tuning, and cost management
- Strong knowledge of AWS data services, including S3, Glue, Lambda, IAM, Redshift, or Athena
- Advanced SQL
- Working knowledge of Python
- Experience designing data platforms supporting machine learning or AI use cases
- Understanding of data governance, security, and privacy requirements in enterprise environments
- Experience working with large international organizations and multiple stakeholders
- Fluent English at C1 level
- Experience with dbt, Airflow or MWAA, Terraform, or other infrastructure as code
- Experience with Snowflake Cortex, Snowpark, Amazon Bedrock, or SageMaker
- Experience with generative AI architectures, including RAG, vector databases, or LLM integration
- Snowflake SnowPro or AWS certification
- Streaming experience with Kafka or Kinesis
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The Company
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