Data Architect
Posted
Summary
Design and own a Snowflake + AWS data platform (ingestion, modelling, governance, performance) that serves BI, ML and GenAI workloads for a startup client's international enterprise customer. Needs ~8 years in data engineering/architecture, advanced SQL and Python, and fluent English; based in Wrocław.
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 AWS. 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 AWS, 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. AWS Glue, Lambda, Kinesis, Step Functions, Snowpipe, dbt)
- Prepare the platform for AI workloads, including feature data, training datasets, vector/embedding storage, and RAG data pipelines (e.g. Snowflake Cortex, Amazon Bedrock, SageMaker)
- Set standards for data quality, lineage, metadata, and governance, including access control, masking, and GDPR compliance
- 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 AWS data services (S3, Glue, Lambda, IAM, Redshift/Athena or similar)
- 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, or Amazon Bedrock / SageMaker
- Experience with GenAI architectures (RAG, vector databases, LLM integration)
- Snowflake SnowPro or AWS certifications (e.g. Data Engineer / Solutions Architect)
- Streaming (Kafka, Kinesis)
Skills
- AI
- Airflow
- Analytics
- Athena
- AWS
- AWS Bedrock
- Aws Glue
- Data Engineering
- Data Governance
- Data Modeling
- Data Pipelines
- Data Quality
- dbt
- Gdpr
- Generative AI
- IAM
- Infrastructure as Code
- Kafka
- Kinesis
- Lambda
- LLM
- Machine Learning
- Python
- Redshift
- S3
- SageMaker
- Snowflake
- Snowpark
- SQL
- Terraform
- Vault
- Vector Databases
