Data Platform Engineer (Snowflake)

We are seeking a highly skilled Data Platform Engineer to join our client's team and help architect, build, and optimize our client's enterprise data platform. This role will focus on Snowflake as the core data warehouse technology and will be instrumental in designing scalable data models, defining semantic layers, and enabling advanced analytics and AI-driven use cases.

What can we offer:

• Long-term cooperation based on Employment contract
• Work mode: Hybrid in Warsaw (2-3 days per week from office)

Responsibilities:

• Design and implement robust, scalable, and performant Snowflake database schemas and data models.
• Define and maintain semantic layers to support self-service analytics and machine learning workflows.
• Develop and optimize SQL for data transformation, aggregation, and consumption.
• Integrate data from diverse sources including relational databases, interface files, message queues, and APIs.
• Collaborate with data scientists, analysts, and application developers to ensure data accessibility and usability.
• Implement and maintain data governance, security, and RBAC policies within Snowflake.
• Monitor and tune performance of data pipelines and warehouse queries.
• Contribute to the development of data platform standards, best practices, and documentation.

Qualifications:

• 5+ years of experience in data engineering, database design, or data platform development.
• Strong expertise in Snowflake, including schema design, performance tuning, and data sharing.
• Proficiency in SQL, with experience writing complex queries and stored procedures.
• Experience with data integration from varied sources (e.g., APIs, flat files, message queues).
• Familiarity with ETL/ELT frameworks and orchestration tools (e.g., dbt, Airflow).
• Understanding of semantic modeling and analytics enablement.
• Solid programming skills in Python or Java for data processing and automation.
• Knowledge of data governance, security, and access control principles.

Preferred Knowledge & Tools:

• Experience with AI/ML workflows and enabling data access for model training and inference.
• Familiarity with cloud platforms (e.g., AWS, Azure, GCP) and cloud-native data services.
• Exposure to BI tools (e.g., Tableau, Power BI) and how they interact with semantic layers.
• Experience with CI/CD and DevOps practices in data engineering.