Integrations Data Engineer (LATAM) - 21163

Job title: Integrations Data Engineer
Location: LATAM (Remote)
Type: Full-Time
Compensation: $4,500–$6,000 USD

About the company
The company builds the decision intelligence platform for independent wealth management. We are expanding our four-person Success Engineering team to deepen data reliability and scale our integration infrastructure.

Role Overview
You will own how customer data enters the platform and how it is modeled. As the deeper backend seat of a two-person integration function—paired directly with a customer-facing data engineer—you will build new integrations and custom ETL pipelines from scratch while taking point on our most complex pipeline challenges.

Key Responsibilities

• Architect, build, and maintain complex API integrations and custom ETL pipelines for customer and financial data.
• Design and optimize data models in PostgreSQL, ensuring data integrity, reconciliation, and high performance at scale.
• Partner with the customer-facing data engineer to resolve backend integration bottlenecks and elevate data infrastructure reliability.
• Establish best practices for data ingestion, pipeline health, and backend data processing.

Must-Haves

• Experience: 3+ years in backend or data engineering operating at a strong technical bar.
• Core Tech: Strong hands-on proficiency with Python and Django.
• ETL & APIs: Proven experience building custom API integrations and end-to-end ETL pipelines for financial or customer data.
• Database Mastery: Strong SQL and PostgreSQL depth at volume, including schema design, data modeling, and reconciliation.
• Communication: Fluent technical English (written and spoken) to collaborate effectively across distributed teams.

Nice-to-Haves

• Background in SaaS or Fintech (wealth management domain knowledge is a major plus).
• Domain familiarity with portfolios, holdings, transactions, and corporate actions.
• Hands-on experience working with custodial feeds, CRMs, or market-data provider APIs.
• Prior ownership of large-scale data migrations.
• Exposure to NoSQL, columnar, or time-series databases.
• Experience setting up pipeline monitoring, logging, and automated alerting systems.