Analytics Analyst

Design, build, and maintain scalable data pipelines (ETL/ELT) that ingest, clean, transform, and load data from multiple sources into the data warehouse/lake.

Develop and maintain data models, schemas, and transformations (e.g., with dbt) that produce reliable, well-documented, analytics-ready datasets.

Ensure data quality, integrity, and governance: implement validation, testing, monitoring, alerting, and documentation across the data platform.

Optimize storage, query performance, and pipeline cost/efficiency, and troubleshoot failures with robust error handling and retries.

Partner with data analysts, data scientists, and business stakeholders to understand their needs and deliver the datasets and infrastructure they require.

Apply security and compliance best practices to data handling (access controls, encryption, PII protection), coordinating with Security/IT as needed.

Software / tools: Strong SQL and proficiency in Python for data engineering; hands-on experience with ETL/ELT and orchestration tools (e.g., Airflow, dbt), cloud data warehouses (BigQuery, Snowflake, or Redshift), and a major cloud (GCP/AWS/Azure). Version control (Git).

Specific knowledge: Data modeling and warehousing (dimensional modeling, star schemas), batch and streaming patterns, data quality and governance, and performance optimization. Experience in fintech or high-volume transactional environments is valued.

Expected results: Reliable, well-documented, high-quality data pipelines and datasets with strong uptime; timely, accurate data available to downstream teams; and efficient, cost-effective data infrastructure.

Skills: Strong analytical and problem-solving skills, attention to detail, ownership and reliability, clear communication, and collaboration across technical and business teams.

Desirable (not essential): Experience with streaming (Kafka / Pub/Sub), infrastructure-as-code, data catalog/observability tools, and system integrations; intermediate/advanced English (Portuguese a plus); and prior experience in a similar Data Engineer role.