Design, develop, and maintain analytical data models with a focus on dimensional modeling (star schema) to support dashboards and credit analytics.
Build and optimize data pipelines using orchestration and distributed processing tools.
Develop ingestion, transformation, and data validation routines in data lake and data warehouse environments.
Partner with business, operations, and technology teams to understand needs and translate requirements into efficient data structures.
Perform exploratory analyses, identify patterns, and support decision-making with data-driven insights.
Document models, transformation rules, data dictionaries, and ingestion workflows.
Support squads and stakeholders in building KPIs, metrics, and analytical views.
Monitor database performance and propose continuous improvements to architecture, governance, and data quality.

Apache Airflow for pipeline orchestration.
PySpark for distributed processing and handling large volumes of data.
Python applied to data engineering and data science.
Pandas and NumPy for data manipulation and analysis.
Advanced SQL for querying, modeling, and optimization.
Experience with AWS Cloud, including:
AWS Glue (ETL/ELT)
Amazon Redshift (data warehouse)
Querying data stored in Amazon S3
Experience with dimensional modeling (facts, dimensions, star schema).
Hands-on experience with data pipelines and data governance best practices.
Experience in the financial sector, especially in credit operations or credit products.
Familiarity with visualization tools (Power BI, Tableau, Looker).
Knowledge of version control (Git) and software development best practices.
Understanding of data quality and data cataloging (Data Quality, Data Catalog).

Data Scientist

Key skills