Senior Data Engineer

BlackLine is a leading provider of cloud software automating financial close processes, committed to modernizing finance and accounting functions globally. The role is part of the Business Intelligence & Analytics Engineering team in Krakow, focused on the AI-powered Invoice-to-Cash (I2C) SaaS solution.

• Strong hands-on experience with Snowflake, including data modelling, performance tuning, and security features.
• Proficiency with Power BI: DAX, Power Query, semantic models, row-level security, incremental refresh.
• Experience with production ELT/ETL pipelines using tools such as dbt, Apache Airflow, or Azure Data Factory.
• Expert-level SQL and proficiency in at least one general-purpose language (preferably Python).
• Experience using AI-driven development tools (e.g., Cursor, Claude).
• Strong data quality and observability mindset integrating monitoring and testing.
• Ability to work independently and take ownership of data products.
• Excellent communication skills to bridge technical concepts and business requirements across time zones.

Nice to have:

• Experience in regulated industries (finance, accounting, fintech) with focus on data governance and compliance.
• Familiarity with Microsoft Fabric and Azure/AWS cloud data architectures (Azure Synapse, AWS Glue).
• Experience with streaming data ingestion using Kafka, Azure Event Hubs, or similar.
• Knowledge of Responsible AI principles and building trustworthy AI/ML pipelines.
• Contributions to open-source data tooling or interest in modern data stack patterns.

• Design, implement, and maintain ELT/ETL data pipelines from operational systems into Snowflake.
• Develop enterprise-grade Power BI reports and dashboards with semantic models and row-level security.
• Shape and evolve end-to-end data architecture on Snowflake ensuring scalability and cost efficiency.
• Apply best practices in data modelling producing modular, well-tested data models.
• Use AI development tools like Cursor and Claude to accelerate development and optimize queries.
• Implement data quality frameworks, automated testing, and monitoring in pipelines.
• Conduct code reviews, documentation, and knowledge sharing on data modelling and BI best practices.
• Collaborate with Software Engineers, Product Managers, and Finance stakeholders to meet data needs.