Senior Data Engineer (Snowflake)

Location: Krakow, Poland; hybrid work with at least 3 office days per week for candidates within reasonable commuting distance.

Company

hirify.global provides cloud software that automates and controls the financial close process, including an AI-powered Invoice-to-Cash platform.

What you will do

• Design, implement, and maintain ELT/ETL pipelines that transform operational, event-stream, and third-party data into analytics-ready Snowflake datasets.
• Develop Power BI semantic models, enterprise reports, dashboards, row-level security, and governed BI capabilities.
• Contribute to Snowflake data platform architecture, ensuring scalability, reliability, and cost efficiency.
• Build modular, tested, documented data models with data quality frameworks, monitoring, alerting, and observability.
• Use AI-assisted development tools such as Cursor and Claude to improve pipeline development, query optimization, and documentation.
• Collaborate with software engineers, product managers, and finance stakeholders while contributing through code reviews and knowledge sharing.

Requirements

• Strong hands-on experience with Snowflake, including data modelling, performance tuning, cost optimization, clustering or dynamic tables, and security and governance.
• Solid Power BI experience, including DAX, Power Query, semantic models, enterprise report design, row-level security, incremental refresh, and the Power BI service.
• Production ELT/ETL pipeline experience with dbt, Apache Airflow, Azure Data Factory, or equivalent orchestration tools.
• Expert-level SQL and working knowledge of a general-purpose programming language, preferably Python.
• Experience with AI-driven development tools and a strong data quality and observability mindset.
• Ability to independently own initiatives, translate business requirements into data products, and communicate across time zones.

Nice to have

• Experience in finance, accounting, fintech, or another regulated industry.
• Microsoft Fabric, Azure Synapse, Azure Data Lake, AWS Glue, or S3 data lake experience.
• Streaming or event-driven ingestion with Kafka, Azure Event Hubs, or similar technologies.
• Knowledge of Responsible AI principles and auditable AI/ML data infrastructure.
• Open-source data tooling experience or interest in modern data stack patterns.

Culture & Benefits

• Kind, open, and inclusive workplace culture.
• Professional development seminars and learning opportunities.
• Inclusive affinity groups supporting diversity and belonging.
• Combination of virtual and in-person collaboration designed to support teamwork.