Mid Data Engineer

Klar

Data Engineer developing data pipelines and web scraping services for Klar, a fintech company in Mexico. Collaborating with Revenue Ops team and handling data reliability and workflows.

Posted 7/28/2026full-timeMexico City • 🇲🇽 MexicoJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and maintaining production Python services, with a strong focus on web scraping, data pipelines, and cloud-native solutions. Proficient in collaborating with cross-functional teams to enhance data reliability and automate workflows.

Highest-signal resume keywords

Python Backend DevelopmentWeb Scraping AutomationStreaming Data SystemsCloud Services on AWSDatabase Management with PostgreSQL

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonFastAPIScrapyPostgreSQLSQLAlchemyKafkaPytestDockerKubernetesTerraform

Soft Skills

Technical CommunicationCollaborationProblem-Solving

Tools & Technologies

AWSPlaywrightPrometheusOpenTelemetryCI/CD Tools

Industry Keywords

Revenue OperationsFintechData EngineeringWeb ScrapingData Pipelines

Tech Stack

Tools & technologies

Amazon RedshiftAWSCloudDockerKafkaKubernetesPostgresPrometheusPythonSparkTerraform

About the role

Key responsibilities & impact

Build and maintain web scraping, extract data, create data pipelines and infrastructure.
In charge of creating real-time processes and alerts, improving data reliability and quality, and automating Revenue Ops workflows.
Collaborate asynchronously with product, operations, and engineering stakeholders.
Develop and maintain scraping services end to end, from credential handling and extraction flows to parser reliability and operational tooling.
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.

Requirements

What you’ll need

Mandatory 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, HTTP 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.
Desirable 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.
Familiarity with proxy providers, browser fingerprint hardening, captcha/error classification, and safe live diagnostics for scraping systems.
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.

Benefits

Comp & perks

Competitive salary based on performance and experience
Chance of earning Klar stock options
15 days of paid vacation per year; plus extended maternity and paternity leaves
Vacation premium
30 days of Christmas bonus
Food vouchers
Medical Insurance
Computer device
Wellhub subscription to offer mental and physical health
Sponsored coaching and therapy sessions via a Mental Health platform
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
A world class team that helps you evolve your skills in areas you're interested in