Staff Data Engineer
Job description
About the role
At Kueski, we are dedicated to improving the financial lives of people in Mexico, operating as the leading buy now, pay later (BNPL) and online consumer credit platform across Latin America since 2012. We are currently seeking a Staff Data Engineer to drive the technical strategy, architecture, and long-term evolution of our data platform, owning platform direction and establishing engineering standards. This role is ideal for a deeply technical leader who thrives in complex, ambiguous environments and can deliver scalable data solutions from problem definition through production deployment. You will partner cross-functionally with Data Science, ML, Analytics, Platform, and Product teams to build reliable, high-impact data systems that directly support Kueski's business objectives. In this position, you will mentor engineers at all levels, elevate technical quality, and make architecture decisions that ensure the long-term health and scalability of our data infrastructure. You will establish and champion data engineering methodologies, best practices, and technical standards while leading large-scale initiatives spanning batch, streaming, AI-centric, and governance capabilities.
Key facts
What you'll do
Define and drive the data engineering technical strategy, architecture decisions, and platform roadmap aligned to company objectives.
Lead and deliver large-scale, complex data initiatives spanning multiple teams and iterations, guiding ambiguous problem definitions through stable production deployment.
Design robust, scalable data architectures for both batch and streaming workloads that support Kueski's long-term business needs at scale.
Shape and execute an AI-centric data strategy that leverages the latest AI technologies to accelerate value delivery and enable trusted self-service data consumption.
Identify the limits of existing tools or processes and lead the design and build of new capabilities when current solutions fall short of requirements.
Shape, standardize, and champion data engineering methodologies, best practices, and technical standards for the team and department.
Develop and own CI/CD pipelines and infrastructure-as-code for reliable, automated data platform operations and deployment workflows.
Drive data quality, observability, and governance programs across the data platform to ensure reliability and trust in organizational data assets.
Apply advanced data cleansing techniques to facilitate data consumption and improve data quality across the platform and downstream systems.
Partner cross-functionally with Data Science, ML, Analytics, Platform, and Product teams to deliver data-driven solutions end-to-end with high impact.
Represent data engineering in cross-organizational initiatives and support efforts outside the core area of responsibility to ensure alignment.
Mentor and guide Data Engineers at all levels, challenging assumptions constructively and elevating team quality through code review, pairing, and coaching.
Maintain deep expertise in data engineering at scale, focusing on architecture design, performance optimization, and production operations for critical systems.
Leverage AI-enabled tools for coding, productivity, and system design, including the implementation of AI adjacent infrastructure such as MCP Server, RAG, and related technologies.
Demonstrate expert-level programming in Python, strong SQL fundamentals, and proficiency in Scala or Java, with optional skills in Typescript.
Lead the design, implementation, and optimization of expert-level Apache Spark applications and distributed data processing patterns.
Requirements
Deep expertise in data engineering at scale, including architecture design, performance optimization, and production operations in complex environments.
Proven leadership in delivering large-scale, complex data platform initiatives, navigating ambiguous problem scoping, and driving solutions to stable production.
Experience using AI-enabled tools for coding, productivity, and system design, including implementing AI adjacent infrastructure such as MCP Server, RAG, and similar technologies.
Expert-level proficiency in Python, strong SQL fundamentals, and additional experience with Scala or Java is a significant advantage.
Expert-level experience with Apache Spark and deep knowledge of distributed data processing patterns and optimization techniques for high-volume workloads.
Extensive experience designing, building, and operating robust, production-grade data pipelines for both batch and near-real-time processing scenarios.
Strong command of data modeling practices, including star schemas and dimensional modeling, to support efficient analytics and reporting.
Strong command of big data cloud services such as AWS or Google Cloud, along with data platforms like Databricks, to deploy and manage scalable solutions.
Proven experience defining and implementing CI/CD pipelines and infrastructure-as-code to ensure reliability, repeatability, and speed in data platform operations.