Analytics Engineer

About PayJoy

PayJoy, a Public Benefit Corporation, is a mission-first credit provider dedicated to helping under-served customers in emerging markets to achieve financial stability and success. Our patented technology for secured credit provides an on-ramp for new customers to enter the credit system. Through PayJoy’s point-of-sale financing and card offerings, customers gain access to a modern quality of life. PayJoy’s credit also allows our customers to seize opportunities as micro-entrepreneurs, and acts as insurance for tough times. Through our cutting-edge machine learning, data science, and anti-fraud AI, we have served over 18 million customers as of 2025 while achieving solid profitability for sustainable growth.

This role

The Data Analyst is responsible for designing, building, and maintaining the data infrastructure, pipelines, and models that make clean, reliable data available across PayJoy. This role transforms raw data from multiple sources into well-structured, trustworthy datasets in the data warehouse/lake, enabling analytics, reporting, and data-driven products. As the foundation of the data stack, the Data Engineer ensures data is accurate, timely, scalable, and well-governed so analysts, data scientists, and business teams can rely on it to make informed decisions.

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.

100% Company-funded Health for employees and immediate family members

Life Insurance

Indefinite-term contract

20 days of vacations, unlimited sick leave

$2,000 USD annual Co-working Travel perk

$2,000 USD annual Professional Development perk

Phone finance, headphone benefit, home office equipment allowance and wellness perks

Catered lunches

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