Data Engineer
Summary
Designs and maintains Snowpark/DBT workflows and data pipelines in a banking-focused data warehouse, optimizing SQL queries, automating testing, and collaborating with stakeholders to ensure data quality and reliability.
Banking
B2B up to 170zł/h
Hybrid work in Gdańsk - 3 days per week in the office
Long-term
Main Responsibilities
As a Database Engineer, you will be responsible for:
Designing, implementing, maintaining, and optimizing Snowpark and DBT workflows
Building and improving scalable data transformation pipelines within a modern data warehouse environment
Developing and optimizing complex SQL queries, data models, and database processes
Supporting the design and implementation of CI/CD pipelines for data engineering workflows
Building and improving automated testing frameworks to ensure data quality and reliability
Monitoring recurring and monthly production jobs, identifying failures, performance issues, or data inconsistencies
Troubleshooting data pipeline and database-related issues and implementing long-term improvements
Optimizing existing workflows with a focus on performance, stability, maintainability, and automation
Supporting the development and implementation of AI agents and exploring opportunities to use AI to automate or improve data-related processes
Collaborating with business stakeholders to understand requirements and translate them into technical solutions
Working closely with engineering, data, and business teams across the organization
Documenting implemented solutions, workflows, and technical processes
Key Requirements
SQL – strong practical knowledge, including writing and optimizing complex queries
DBT – hands-on experience designing and maintaining data transformation workflows
Data Warehousing – good understanding of data warehouse architecture, data modeling, transformation, and data processing concepts
Snowpark / Snowflake ecosystem – experience working with Snowpark or similar modern cloud data platforms
CI/CD – experience implementing or maintaining automated deployment pipelines
Test automation – understanding of automated testing approaches within data engineering environments
AI agent development / creation – practical exposure to building AI-powered agents or implementing agent-based automation
Experience with monitoring, troubleshooting, and optimizing scheduled data processing jobs
Strong communication skills and ability to cooperate with both technical and non-technical stakeholders
Professional working proficiency in English
Nice to Have
Previous experience within banking or financial services
Knowledge of Credit Risk processes, data, or systems
Experience working with large-scale enterprise data environments
Understanding of data governance, data quality, and regulatory requirements within financial institutions