Staff Data Engineer (Power BI / Snowflake)

It’s fun to work in a company where people truly believe in what they’re doing!

At BlackLine, we’re committed to bringing passion and customer focus to the business of enterprise applications.

Since being founded in 2001, BlackLine has become a leading provider of cloud software that automates and controls the entire financial close process. Our vision is to modernize the finance and accounting function to enable greater operational effectiveness and agility, and we are committed to delivering innovative solutions and services to empower accounting and finance leaders around the world to achieve Modern Finance.

Being a best-in-class SaaS Company, we understand that bringing in new ideas and innovative technology is mission critical. At BlackLine we are always working with new, cutting edge technology that encourages our teams to learn something new and expand their creativity and technical skillset that will accelerate their careers.

This role is specifically for our AI-powered Invoice-to-Cash solution, a market-leading platform that uses intelligent automation to help companies collect cash faster, unlock working capital, and supercharge efficiency in their accounts receivable processes.

Work, Play and Grow at BlackLine!

As the founding Staff Data Engineer for our new Engineering Hub in Krakow, you are stepping into a role with rare scope and influence. You will be the data backbone of a mission-critical, AI-powered SaaS product, owning the architecture and delivery of the analytics and data platform that our customers and internal teams depend on to make decisions worth millions of dollars.

You will define how data flows from our transactional systems into Snowflake, how it is modeled for reliability and performance, and how it surfaces as trusted, actionable insights through Power BI. You will set the standard for data engineering craft in this hub, mentor engineers who join after you, and operate at the intersection of data infrastructure, product analytics, and enterprise reporting. As a forward-thinking leader in our mission to deliver autonomous finance, you will leverage modern AI development tools like Claude and Cursor to accelerate delivery and raise the quality bar across the data platform.

• Architect and Own the Data Platform: Design and evolve the end-to-end data architecture, from ingestion and transformation to serving, on Snowflake, ensuring it is scalable, cost-efficient, and built for the reliability standards of an enterprise SaaS product.
• Build and Govern Data Pipelines: Design, implement, and maintain robust ELT/ETL pipelines that reliably move and transform data from operational systems (microservices, event streams, third-party sources) into well-modeled, analytics-ready datasets in Snowflake.
• Deliver Trusted Analytics with Power BI: Lead the design and development of enterprise-grade Power BI reports and dashboards for both internal stakeholders and customer-facing analytics, establishing semantic models, row-level security, and a governed BI layer.
• Champion Modern Data Modelling: Drive the adoption of best-in-class transformation practices, establishing modular, well-tested, and documented data models that the whole team can trust and build on.
• Champion AI-Accelerated Development: Utilize and promote agentic development tools (e.g., Cursor, Claude) to accelerate data pipeline development, query optimization, and documentation — and help the broader team adopt this mindset.
• Set Data Quality and Observability Standards: Implement data quality frameworks, automated testing, and monitoring across the data platform. Build the tooling and culture that catches data issues before they reach customers or business decisions.
• Mentor and Elevate: Act as a technical leader and mentor for data engineers in the hub. Elevate the team’s craft through code reviews, documentation standards, and knowledge sharing on data modelling, Snowflake optimization, and BI best practices.
• Collaborate Across the Organization: Partner closely with Software Engineers, Product Managers, and Finance stakeholders to understand data needs, translate them into reliable data products, and communicate data platform capabilities and constraints clearly.

• Demonstrated expertise in using AI-driven development tools (e.g., Cursor, Claude) to significantly improve data engineering velocity and quality.
• Deep expertise with Snowflake, including data modelling, performance tuning, cost optimization, clustering, dynamic tables, and Snowflake-native security and governance features.
• Advanced Power BI proficiency: end-to-end ownership of semantic models (DAX, Power Query), enterprise report design, row-level security, incremental refresh, deployment pipelines, and Power BI service administration.
• Strong experience building and operating production ELT/ETL pipelines using tools such as dbt, Apache Airflow, Azure Data Factory, or equivalent orchestration frameworks.
• Proficiency in SQL at an expert level and working knowledge of at least one general-purpose language (Python preferred) for data processing and automation.
• Proven track record of technical leadership, with the ability to drive complex, cross-functional data initiatives from ambiguous requirements to successful, well-adopted data products.
• A strong data quality and observability mindset — you build monitoring, alerting, and testing into data pipelines from the start, not as an afterthought.
• Excellent written and verbal communication skills; comfortable translating between technical data concepts and business requirements across time zones.

• Experience in a regulated industry such as finance, accounting, or fintech, with sensitivity to data governance, auditability, and compliance requirements.
• Familiarity with cloud-native data architectures on Azure or AWS — particularly Azure Synapse, Azure Data Lake, AWS Glue, or S3-based data lake patterns.
• Experience with streaming or event-driven data ingestion using Kafka, Azure Event Hubs, or similar, feeding near-real-time analytical workloads.
• Knowledge of Responsible AI principles and experience building data infrastructure that supports trustworthy, auditable AI/ML pipelines.
• A history of contributing to open-source data tooling or a passion for exploring emerging patterns in the modern data stack.
• Experience standing up a data practice from scratch in a greenfield or hub-expansion context, including data cataloguing, lineage, and self-serve BI enablement.