Senior Data Engineer (Ingeniero de Datos Senior)

Ready to accelerate your career?

Clara is the fastest-growing company in Latin America. We've built the leading solution for companies to make and manage all their payments. We already help over 20,000 large and growing businesses operate with agility and financial clarity through locally issued corporate cards, bill pay, financing, and a powerful B2B platform built for scale.

Clara is backed by some of the most successful investors in the world, including top regional VCs like monashees, Kaszek, and Canary, and leading global funds like Notable Capital, Coatue, DST Global Partners, ICONIQ Growth, General Catalyst, Citi Ventures, SV Angel, Citius, Endeavor Catalyst, and Goldman Sachs - in addition to dozens of angel investors and local family offices. 

We’re building the financial infrastructure that powers high-performing organizations across the region. We invite you to join us if you want to be part of a fast-paced environment that will accelerate your career and support you to do some of the best work of your life alongside a passionate and committed team distributed across the Americas.

 

Senior Data Engineer (Ingeniero de Datos Senior) - Colombia

CLARA's Data Engineering Team is looking for a highly professional, experienced, and innovative Senior Data Engineer to architect and build robust data infrastructure that powers our analytics, reporting, and emerging AI capabilities. You'll work alongside developers, data analysts, and data scientists to create, deploy, and maintain complex banking and financial data systems that enable data-driven decision making across the organization.

Responsibilities

Your main focus will be to design and build reliable data pipelines that serve analytics teams, business stakeholders, and AI applications. You'll leverage modern tooling (including AI-assisted development) to efficiently develop data infrastructure, improve existing processes, create data models, and mentor junior team members. You will be responsible for the maintenance and operation of these systems, collaborating across teams to provide high-quality, fast-delivery data for the company.

Core Responsibilities:

Data Pipeline Engineering & Analytics Infrastructure

• Build, integrate, and maintain batch and streaming data pipelines following principles of reliability, scalability, and maintainability
• Design and implement data models for analytics, reporting, and business intelligence using dimensional modeling and lakehouse architectures
• Ensure data quality, lineage, and monitoring across all pipelines, understanding and resolving issues proactively
• Implement data lake/lakehouse architectures following best practices: partitioning strategies, zone organization (bronze/silver/gold), avoiding data swamps
• Integrate data from diverse sources: databases, APIs, event streams (Kafka, Kinesis), third-party services, and CDC processes
• Optimize query performance and data access patterns for analytics workloads and dashboard performance

Modern Data Platform & AI-Ready Infrastructure

• Build feature pipelines and data scaffolding that enable ML model training and AI applications for internal teams and external clients
• Design reusable data frameworks and patterns that accelerate both analytics and AI development
• Implement data APIs and microservices that expose curated datasets to downstream consumers
• Create observability systems for data quality, freshness, and pipeline health across all use cases

Development Excellence & AI-Assisted Productivity

• Leverage AI tools (coding assistants, LLMs) daily to accelerate development, improve code quality, and increase productivity
• Implement CI/CD pipelines with automated testing, validation, and deployment for data assets
• Enforce DevOps culture and best practices within the team
• Document your work thoroughly to create solid foundations for team members and future reference

Collaboration & Leadership

• Collaborate with data analysts to understand reporting requirements and optimize data models for BI tools
• Partner with data scientists and ML engineers on feature engineering and model data requirements
• Work with product and engineering teams to meet company goals and deliver data products
• Mentor junior and mid-level engineers through code reviews, pair programming, and knowledge sharing
• Lead architectural discussions and contribute to long-term platform strategy
• Enforce data security and data privacy best practices across all systems

Requirements

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