At Kueski, we're dedicated to improving the financial lives of people in Mexico. Since 2012, we've been the leading buy now, pay later (BNPL) and online consumer credit platform in Latin America, known for our innovative financial services. Our flagship product, Kueski Pay, provides seamless payment solutions for both online and in-store transactions, establishing itself as the preferred option for nearly 30% of Mexico's top e-commerce merchants. Notably, we were the first to introduce BNPL on Amazon Mexico.
We're a tech company with a culture geared toward innovation, collaboration, and impact, fostering a strong, diverse, and inclusive workplace. Our commitment to excellence and ethical business practices has earned us multiple industry recognitions. In 2024, we were named one of the World’s Top FinTech Companies by CNBC and recognized as one of the most ethical companies in Mexico by AMITAI. Additionally, we were certified as a Best Place to Work for LGBTQ+ Equality by HRC Equidad MX 2025 and ranked among the Best Companies for Female Talent by EFY.
Kueski is seeking a Staff-level Data Engineer to drive the technical strategy, architecture, and long-term evolution of our data platform.
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.
As a key leader in Data Engineering, you will own platform direction, establish engineering standards, and lead large-scale initiatives across batch, streaming, AI-centric, and governance capabilities.
Reporting to the Sr Manager of Data Engineering, you will partner across Data Science, ML, Analytics, Platform, and Product to build reliable, high-impact data systems.
You will mentor engineers at all levels, elevate technical quality, and make architecture decisions that directly support Kueski’s business objectives and long-term platform health.
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—from ambiguous problem definition through production deployment
Design robust, scalable data architectures (batch and streaming) that support Kueski's long-term business needs at scale
Demonstrated success shaping and executing an AI-centric data strategy that leverages the latest AI technologies to accelerate value delivery, enable trusted self-service data consumption, and strengthen data quality, governance, and organizational decision-making.
Identify the limits of existing tools or processes; lead the design and build of new capabilities when current solutions fall short
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
Drive data quality, observability, and governance programs across the data platform
Apply data cleansing techniques to facilitate data consumption and quality across the platform
Partner cross-functionally with Data Science, ML, Analytics, Platform, and Product teams to deliver data-driven solutions end-to-end
Represent data engineering in cross-organizational initiatives; support and lead efforts outside the core area of responsibility
Mentor and guide Data Engineers at all levels; constructively challenge assumptions and elevate team quality through code review, pairing, and coaching
Deep expertise in data engineering at scale: architecture design, performance optimization, and production operations
Proven leadership delivering large-scale, complex data platform initiatives—from ambiguous problem scoping through stable production
Experience using AI-enabled tools for coding, productivity, and system design. Including implementation of AI adjacent infra such as MCP Server, RAG, etc.
Expert-level programming in Python; strong SQL fundamentals; Scala/Java is a plus. Typescript is optional.
Expert-level Apache Spark experience; deep knowledge of distributed data processing patterns and optimization techniques
Extensive experience designing and building robust, production-grade data pipelines (batch and near-real-time)
Deep understanding of data modeling practices such as star schemas and dimensional modeling.
Strong command of big data cloud services (i.e., AWS, Google Cloud) and data platforms such as Databricks.
Proven experience defining and implementing CI/CD pipelines and infrastructure-as-code (IaC) for data workloads
Proven experience working with modern data architectures such as medallion layer, data lakehouses, data products, and adjacent patterns.
Strong grasp of software design patterns, SDLC best practices, and non-functional requirements at scale
Track record of mentoring and elevating data engineering teams; recognized as a technical leader and subject matter expert
Broad collaboration experience with ML, Analytics, Platform, and Product teams on cross-functional data initiatives
Experience driving data quality, observability, and governance programs at scale.
At Kueski we embrace diversity in all forms, systematically promote equity, and ensure everyone feels included with a sense of belonging. We are committed to the full inclusion of all qualified candidates. As part of this commitment, we will make efforts to ensure reasonable accommodations are made during the hiring process. If reasonable accommodation is needed, please let the Talent Acquisition team know.