BI Engineer

Creditstar

About The Position

Creditstar Group is an international fintech company headquartered in Tallinn, Estonia, operating across multiple European markets. We build data-driven financial products and are looking for engineers who want to have a real impact in a fast-moving environment. We are seeking a skilled BI Engineer with 3–7 years of experience to support data-driven decision-making. The ideal candidate will have strong expertise in data modeling, SQL, and modern data stack tools, familiarity with credit or lending metrics is a strong plus, as you'll be working closely with risk and finance teams where domain context matters as much as technical skill.

Requirements

Strong proficiency in SQL
Hands-on experience with Power BI and DAX.
Experience with Snowflake or other cloud data warehouses
Solid understanding of data modeling concepts
Experience with dbt or similar data transformation tools
Strong analytical and problem-solving skills

Nice To Haves

Experience in the lending/financial services domain
Knowledge of Python or scripting for data analysis
Familiarity with ETL/ELT processes
Exposure to cloud platforms (AWS, Azure, or GCP)
Understanding of data governance and data quality frameworks.

Responsibilities

Design, build, and maintain Power BI dashboards that serve as the primary reporting layer for finance, risk, and product teams.
Owning both the accuracy and performance of what gets published.
Write efficient, well-structured SQL queries against Snowflake to extract, validate, and shape data for reporting and ad-hoc analysis.
Design and implement scalable semantic data models in Power BI including DAX measures, star-schema structures, and calculated tables, ensuring reports perform reliably at scale.
Build and manage data transformation logic using dbt, collaborating with the data engineering team to ensure the right data is available, structured correctly, and ready for the BI layer.
Partner with finance, risk, and product stakeholders to translate business questions into well-defined reporting requirements.
Own data validation and quality checks across your reporting surfaces.
Contribute to metric governance: documenting definitions, business logic, and calculation methodology so that key terms like "active loan" or "default rate" mean the same thing across every report and every market.
Identify opportunities to improve existing reports, reduce manual processes, and increase self-service capability across business teams.