Staff Data Engineer

Clara is the leading platform in Latin America for companies to execute and manage every payment operation. More than 50,000 leading and fast-growing businesses run on our corporate cards, bill pay, financing and B2B payments, and in 2025 the Financial Times named Clara the region’s fastest-growing company. We’re passionate about helping every company we work with become more effective—and, through that, raising the region’s competitiveness. We’re ~400 people, backed by Notable Capital, Coatue, Kaszek, monashees, DST Global, ICONIQ, General Catalyst and Goldman Sachs, among others.

Staff Data Engineer

We are looking for a Staff Data Engineer to be the technical backbone of our Data organization. You will be part of the team owning the architecture of our data platform and making sure our data is ready for the AI era. This is a hands-on, individual-contributor role: you will spend most of your time designing and building, not managing people.

You will:

Own the technical vision and architecture of Clara's data platform, from ingestion and storage to modeling, serving and consumption

Design and build scalable, reliable, cost-efficient data pipelines (batch and streaming) that support analytics, machine learning and operational use cases

Lead our AI enablement efforts: structure, document and expose data (semantic layers, curated datasets, metadata, feature stores, retrieval-ready data) so that people, models and AI agents across the company can use it safely and effectively

Define and drive best practices in data modeling, testing, observability, CI/CD, lineage and data quality

Partner with the Director of Data to translate business and strategic goals into technical roadmaps, and to evaluate build-vs-buy decisions

Implement the technical foundations of our data governance strategy: access control, PII handling, auditability and compliance, which matter in a regulated fintech environment

Advance our self-service data platform so analysts, data scientists and business teams can work autonomously

Collaborate closely with Engineering, Product, Risk, Growth, Marketing and Operations to ensure data is captured correctly at the source

Raise the technical bar of the team through code reviews, design reviews, mentoring and technical documentation, without formal people-management responsibilities

Stretch and adapt to a dynamic and fast-paced work environment

Requirements

10+ years of experience in data engineering or software engineering with a data focus, including significant experience building and scaling data platforms at a top-tier tech company (fintech/startup experience is a plus)

Proven track record designing and operating production-grade data architectures (lakehouse/warehouse, ELT/ETL, orchestration, streaming) at scale

Deep proficiency in SQL and Python; experience with distributed processing frameworks (e.g. Spark) and orchestration tools (e.g. Airflow, Dagster)

Experience building tooling for AI agents or LLM applications on top of enterprise data (text-to-SQL, MCP, RAG pipelines)

Hands-on experience preparing data for ML and AI use cases (feature engineering, vector stores/embeddings, semantic layers, LLM-ready data) or a clear track record of moving quickly into this space

Strong experience with cloud data platforms and infrastructure (AWS, GCP or Azure), plus infrastructure-as-code and CI/CD practices

Strong command of data modeling, data quality, observability and governance best practices

Experience with modern data stack tools (e.g. dbt, Databricks, Snowflake, BigQuery, Kafka)

Ability to make sound architectural trade-offs (scalability, cost, reliability, speed of delivery) and communicate them clearly to technical and non-technical audiences

Strong interpersonal and communication skills; comfortable influencing without authority and working closely with a Director-level leader

Bachelor's degree in Computer Science, Engineering, Math, or another relevant quantitative discipline (or equivalent practical experience)

Nice to have:

Experience in payments, credit, cards or other regulated financial services

Experience with data catalogs, metadata management and data contracts

Open-source contributions or technical writing/speaking

Where we work

This is a hybrid role: you'll split your time between the office, customers and home. We don't set a minimum number of office days for most roles — you'll spend time in the office naturally, and most days during your ramp-up or when your leader asks.

Why Clara

A high talent-density team driven to make an impact in Latin America — low ego, high ownership.

Competitive salary and stock options (ESOP) from day one.

An annual learning budget and accelerated development paths.

English is our working language, with Spanish and Portuguese every day.

How we work

Clarity — Open and direct communication: we say what we mean and give context where it matters.

Simplicity — If it can be simpler, it should be.

Ownership — We own everything we do, end to end. When something needs to happen, we don't wait.

Pride — We hold ourselves to a high standard, and it shows in what we deliver.

ABC (Always Be Changing) — We adapt fast, embrace change, and keep learning.

Inclusivity — Diverse ideas, stories and perspectives build a better Clara.

If you meet the must-haves, apply — the nice-to-haves are a bonus, not a requirement.