Staff Data Engineer

This is a fully remote position, open to applicants in Mexico.

📋 Description

• Establish and steer the technical strategy for data engineering, making architectural decisions and creating a platform roadmap that aligns with company goals.

• Oversee and execute large-scale, intricate data projects—collaborating across multiple teams and iterations—from vague problem identification to full production deployment.

• Create resilient, scalable data architectures (both batch and streaming) that meet Kueski's strategic business needs over the long term.

• Proven success in designing and implementing an AI-driven data strategy that utilizes the latest AI technologies to enhance value delivery, support trusted self-service data access, and improve data quality, governance, and organizational decision-making.

• Recognize the limitations of current tools or processes; lead the development of new capabilities when existing solutions are inadequate.

• Influence, standardize, and advocate for data engineering methodologies, best practices, and technical standards within the team and across the department.

• Create and manage CI/CD pipelines and infrastructure-as-code for dependable, automated operations of the data platform.

• Champion data quality, observability, and governance initiatives throughout the data platform.

• Employ data cleansing methods to enhance data usability and quality across the platform.

• Collaborate with Data Science, ML, Analytics, Platform, and Product teams to provide comprehensive data-driven solutions.

• Represent data engineering in cross-departmental initiatives; support and lead projects beyond the immediate scope of responsibility.

• Mentor and guide Data Engineers at various levels; constructively challenge assumptions and enhance team quality through code reviews, pairing, and coaching.

⛳️ Requirements

• In-depth expertise in data engineering at scale: architecture design, performance optimization, and operational management.

• Proven ability to lead large-scale, complex data platform projects—from initial problem definition to stable production deployment.

• Experience utilizing AI-enabled tools for coding, productivity, and system architecture, including the implementation of AI-related infrastructure such as MCP Server, RAG, etc.

• Proficient programming skills in Python; solid SQL fundamentals; knowledge of Scala/Java is advantageous. Typescript is a bonus.

• Advanced experience with Apache Spark; comprehensive understanding of distributed data processing patterns and optimization strategies.

• Significant experience in designing and constructing robust, production-quality data pipelines (both batch and near-real-time).

• Strong grasp of data modeling techniques, including star schemas and dimensional modeling.

• Proficient in big data cloud services (e.g., AWS, Google Cloud) and data platforms like Databricks.

• Proven track record in defining and implementing CI/CD pipelines and infrastructure-as-code (IaC) for data workloads.

• Familiarity with modern data architectures such as medallion layer, data lakehouses, data products, and related patterns.

• A solid understanding of software design patterns, SDLC best practices, and non-functional requirements at scale.

• History of mentoring and uplifting data engineering teams; acknowledged as a technical authority and subject matter expert.

• Extensive collaborative experience with ML, Analytics, Platform, and Product teams on cross-functional data projects.

• Experience in leading data quality, observability, and governance initiatives on a large scale.

🏝️ Benefits

• Health insurance

• Retirement plans

• Paid time off

• Flexible work arrangements

• Professional development