Senior Data Engineer

Svitla Systems Inc. is looking for a Senior Data Engineer for a full-time position (40 hours per week) in LATAM. Our client is a US-based fintech company that runs a community finance platform where members can borrow and lend small amounts to cover emergency expenses. Launched in 2018, it offers an alternative to payday loans, with borrowers setting their own terms, along with financial education and digital banking services. The platform serves around 2 million users and has facilitated about $1 billion in transactions.

Our client is building the data function of the future: one professional operating a team of AI agents rather than managing a team of people. In this builder-operator role, you will own data science, analysis, business intelligence, risk, fraud, and data engineering across the client’s ecosystem, using agents as the workforce and your judgment as the control layer. You will design the pipelines, systems, and evaluation standards that turn transaction and behavioral data into signals the business can act on, while keeping data quality, lending risk, and fraud detection sharp end to end.

Requirements

• 5+ years as a data or software engineer shipping production systems (not just analysis or reporting).
• Strong Python fundamentals and systems-design judgment, plus deep fluency in SQL and data warehousing.
• Hands-on experience directing AI agents or LLM-based workflows in production, not just experimenting with them.
• A portfolio, GitHub history, or shipped systems that you can point to and discuss in depth.
• Comfort operating with high autonomy and full ownership.

Will be a plus

• Risk and fraud analytics experience in fintech, lending, or payments.
• Credit scoring or alternative underwriting background.
• Track record of building evaluation or observability systems for ML or agent pipelines.
• Background in consumer fintech or marketplaces.
• Experience working in a high-pace, high-accountability environment.

Responsibilities

• Orchestrate AI agents across the full data lifecycle, from ingestion and pipeline health to modeling and reporting, while owning the code and architecture they touch.
• Build and refine evaluation harnesses that keep agent output accurate and auditable.
• Write and review production Python and SQL for pipelines, services, and models.
• Make systems-design decisions on how data infrastructure scales.
• Monitor scoring model performance, default and delinquency trends, and fraud signals, intervening when human judgment must lead.
• Stand up dashboards and BI that give the team a live view of marketplace health.
• Partner with product and risk to ship data-informed decisions fast.