Develop and optimize ETL/ELT pipelines to ingest data from multiple sources (APIs, databases, files) into the data platform (GCP/BigQuery)
Implement data transformations and models using DBT on BigQuery, ensuring quality, performance, and reproducibility of data transformations
Maintain and evolve wholesale credit engines, incorporating business rules and analytical models into data pipelines to support credit decisions
Monitor and improve the performance of queries and jobs in BigQuery, optimizing cost and execution time (FinOps mindset)
Collaborate in multidisciplinary teams (data engineers, software developers) in an agile environment, participating in code reviews, data architecture design, and Scrum/Kanban ceremonies
Ensure data quality and governance by implementing validations, log/monitoring tracking, and security best practices according to BV policies (LGPD, compliance)
Document pipelines, datasets, and model definitions, ensuring knowledge sharing with the team, business users, and other stakeholders

Experience with Google Cloud Platform – especially BigQuery and Cloud Storage
Proficiency in SQL and Python – ability to write optimized SQL queries (BigQuery Standard SQL) and Python scripts for data manipulation and integration
ETL/Orchestration tools – experience building automated, scheduled data pipelines; familiarity with tools such as Apache Airflow/Cloud Composer (workflow orchestration)
DBT (Data Build Tool) – experience using DBT for data transformations in the data warehouse, including creating models, Jinja macros, and tests, with version control via Git
Agile methodologies and DevOps – familiarity with working in sprints (Scrum/Kanban) and CI/CD practices for deploying pipelines (version control, code review, continuous integration pipelines)
Databases and Data Warehousing – knowledge of data modeling and data warehouse principles; understanding of Data Lake concepts and modern data architectures (e.g., layered Trusted/Delivery architecture following BV practices)
Tools and frameworks – use of version control systems (Git), and knowledge of collaboration tools (JIRA, Confluence) for tracking tasks and documentation.

Data Analyst II

Key skills