Uber's first integral credit card, built right here in Mexico. Klar Empresarial, a brand-new B2B solution — Klar Empresarial — bringing agile credit and smart accounts to SMEs who've been ignored by traditional banks for too long. And a full banking license on the horizon, because our customers asked for it and we listened.

We move fast, we think big, and we go all in, because as our CEO puts it: "Growing doesn't always mean going further. Sometimes it means going deeper." Behind all of it is a team of 30+ nationalities, working across Mexico City, Berlin, and Argentina, obsessed with building financial products that are simpler, faster, and fairer than anything that came before. With our head office in Mexico City, and remote tech hubs in Berlin and Argentina, we are always learning something new about another culture or language.

Customer

Obsession - We understand the value Klar can bring to its customers & it's always at the forefront of our decisions. It's in our name & it's what we do. We're currently looking for a Data Engineer to join our Revenue Ops team.

As a Data Engineer your main responsibilities will be to build and maintain web scraping, extract data, create data pipelines and infrastructure. You will be in charge of creating real-time processes and alerts, improving data reliability and quality, and automating Revenue Ops workflows. Work remotely as part of the Revenue Ops team, collaborating asynchronously with product, operations, and engineering stakeholders.

Build and maintain production Python services across APIs, event-driven worker processes, relational persistence, and cloud-backed artifacts. Design streaming and batch data flows that make business data reliable, timely, observable, and usable for Revenue Ops workflows.



Debug real-world scraping and data-processing failures involving external systems, browser automation, retries, providers, and artifact-based diagnostics.

Improve engineering quality through tests, clear documentation, structured logging, metrics, traces, and strategic use of modern AI-powered tools. 2+ years of professional software engineering or data engineering experience building production Python services, with ownership of design, implementation, testing, and operations.

Strong

Python backend experience with FastAPI or similar web frameworks, Pydantic-style validation, async workflows, and typed service boundaries. Required knowledge of streaming data systems and event-driven processing, especially Kafka consumers/producers, partitioning, ordering, delivery semantics, retry/idempotency, backoff, and operational failure handling. Solid database experience with PostgreSQL and SQLAlchemy/Alembic, including schema design, migrations, transactional boundaries, and performance-aware queries.

Practical experience building or maintaining web scraping/browser automation systems with Scrapy, Playwright, sessions, anti-bot constraints, and deterministic parser tests.

Experience handling sensitive credentials or confidential business data, including encryption, secret versioning, redaction, auditability, and least-privilege access patterns. Comfort owning cloud-native services on AWS, including S3,



KMS, containerized deployments, metrics, traces, and production incident debugging.

Advanced

English and clear technical communication; able to read existing architecture, reason from tests and logs, document tradeoffs, and collaborate with product/ops stakeholders. Comfort using modern AI-powered tools strategically to accelerate development, debugging, documentation, and analysis while applying sound engineering judgment.

Experience with SAT, tax, fintech, invoicing, or other Mexican financial workflows.

Experience with worker/master architectures, Kubernetes, Docker Compose, horizontal scaling, worker concurrency, and queue-based scheduling. Strong testing discipline with pytest, integration tests, replay/VCR-style fixtures, static analysis, and CI quality gates such as ruff and pyright.

Experience designing observable systems with structured logging, Prometheus metrics, OpenTelemetry traces, bounded labels, and explicit failure taxonomies.

Bonus: experience with Terraform, DBT, Redshift, Spark, Flink/RisingWave, or data orchestration tools, when relevant to adjacent data platform work. Competitive salary based on performance and experience Medical Insurance A modern centrally located office in Mexico City with free drinks, snacks, and regular social events International work environment with amazing and highly skilled people We trust our highly skilled and diverse team and we're committed to creating a welcoming and inclusive environment for new talents to flourish. We value diversity and welcome all applications regardless of gender, nationality, ethnic and social origin, religion/belief, physical abilities, age, sexual orientation and identity.

📌 Data Modellers (Venustiano Carranza)
🏢 Klar
📍 Venustiano Carranza