Staff Data Engineer

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Kueski

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

💳 Fintech

💸 Finance

👥 B2C

💰 $23.3M Venture Round - Kueski on 2022-10

Fintech • Finance • B2C

Kueski is a Mexico-based digital financial services company and fintech that provides online, unsecured personal loans and microcredits, plus a buy-now-pay-later payment product called Kueski Pay. It serves consumers through a mobile app and website with fast, automated loan decisions (including products that don’t require a credit bureau record), and also offers merchant integrations to enable installment payments. Kueski emphasizes 100% online processes, data encryption, regulatory compliance with Mexican authorities (CONDUSEF/CNBV), customer support, and financial-education resources.

📋 Description

• 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

🎯 Requirements

• 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.

🏖️ Benefits

• Health insurance • Retirement plans • Paid time off • Flexible work arrangements • Professional development