• Own the long-term design and architecture of the enterprise data ecosystem, including ingestion, storage, modeling, lineage, governance, and analytics layers. • Design scalable lakehouse, Delta Lake, and distributed data architectures supporting advanced analytics, operational workflows, and integration across business domains. • Lead enterprise-wide modernization projects: warehouse migrations, domain modeling redesigns, governance uplift, streaming adoption, or cross-cloud data integrations. • Define and enforce standards for data modeling, lineage, metadata, MDM, quality, security, and compliance across all data teams. • Create reusable architectural patterns, frameworks, orchestrations, and platform components adopted across engineering groups. • Solve the most complex technical problems, including distributed system bottlenecks, data quality crises, lineage gaps, and multi-domain data reconciliation issues. • Guide cost optimization strategy for compute, storage, and orchestration workloads across the data platform. • Partner with enterprise architecture, analytics, InfoSec, product, and application engineering to ensure alignment with organizational strategy. • Influence leadership decisions regarding data strategy, platform investments, tooling, and sprint/roadmap priorities. • Mentor senior engineers, conduct design reviews, and provide technical leadership across teams to raise the overall engineering bar.

Role Description We are seeking a Senior Graph Data Engineer to join a strategic initiative focused on evaluating and benchmarking enterprise-scale knowledge graph technologies. This role combines hands-on data engineering, graph database implementation, performance testing, and technical analysis to determine the best technology stack for large-scale graph-based workloads. The ideal candidate is passionate about data platforms, graph technologies, performance optimization, and evidence-driven engineering decisions. You will work closely with engineering and architecture leaders to assess multiple graph database platforms and provide technical recommendations based on measurable results and enterprise requirements. Main Responsibilities - Design and execute large-scale graph database evaluations and benchmarking initiatives. - Extract, transform, and load data from Databricks into multiple graph database platforms. - Build and maintain graph structures, entity relationships, and linkages that replicate production-grade data models. - Implement benchmark workloads using graph query languages such as Cypher, GSQL, openCypher, nGQL, or AQL. - Develop PostgreSQL baseline implementations using recursive CTEs and advanced querying techniques. - Design and build APIs to evaluate real-world query performance and scalability. - Analyze execution plans, indexing strategies, and query optimization opportunities. - Continuously provision, configure, and tear down database environments for controlled testing. - Document findings, benchmark results, technical trade-offs, and recommendations. - Collaborate with architects, data engineers, and technical leadership teams throughout the evaluation process. - Participate in architecture discussions and contribute to strategic technology decisions. Qualifications - 6+ years of experience in Data Engineering, Database Engineering, or Data Platform development. - 3+ years working with enterprise-scale data platforms. - Proven experience designing and executing technology evaluations and performance benchmarks. - Experience working with distributed systems and large-scale data environments. Required Technical Skills - Strong Databricks experience. - Advanced SQL and PostgreSQL expertise. - Deep knowledge of: - Recursive CTEs - Query optimization - Query plan analysis - Indexing strategies - Materialized views - Hands-on experience with at least two graph database technologies, preferably: - Neo4j - TigerGraph - Nebula Graph - ArangoDB - Memgraph - Strong Cypher knowledge. - Experience building APIs for data access and performance testing. - ETL/ELT development experience. - Git and version control best practices. Nice to Have - AWS-native database deployments. - Kubernetes for database workloads. - Knowledge Graph architecture experience. - KYC / AML domain knowledge. - Data modeling for highly connected datasets. - Experience comparing graph databases against relational database solutions. Soft Skills - Strong analytical and problem-solving abilities. - Excellent communication and documentation skills. - Ability to present technical findings to leadership teams. - Ownership mindset and accountability. - Data-driven decision making. - Ability to work independently in ambiguous environments. - Strong collaboration skills within distributed teams. Education - Bachelor's degree in Computer Science, Software Engineering, Information Systems, Data Science, or related field. - Master's degree is a plus. Additional Requirements - Advanced English (C1+ preferred). - Remote position. - Candidates located in LATAM or Mexico. - Availability to collaborate with teams across the Americas time zones. Benefits - 🚀 Integration into global brands and disruptive startups. - 🏠 Remote work/Home office. - 📌 If a hybrid or on-site modality is required, you will be informed from the first session. - ⏰ Schedule aligned with the assigned project/work cell. - 📅 Monday to Friday work schedule. - 🎂 Day off on your birthday. - 🏥 Major medical insurance (applies to Mexico). - 🛡️ Life insurance (applies to Mexico). - 🌎 Multicultural teams. - 📚 Access to courses and certifications. - 🎤 Meetups with special guests from the IT industry. - 🤝 Virtual integration events and interest groups. - 🇺🇸 English classes. - 📈 Opportunities within our different business lines. - 🏆 Proudly certified as a Great Place to Work.

• Design, develop, and maintain scalable, robust data pipelines. • Build data ingestion, transformation, and modeling solutions using Databricks, Spark/PySpark, Cloudera, and Azure Data Factory (ADF). • Ensure the quality, integrity, and usability of data across the entire pipeline. • Implement CI/CD pipelines focused on ETL processes. • Design and maintain Data Lake and Data Warehouse solutions, applying data governance best practices. • Apply FinOps concepts to optimize cloud data processing costs. • Develop and document data workflows, processes, and architectures. • Perform technical troubleshooting of pipelines and data infrastructure. • Work with concepts and structures such as Bronze, Silver, and Gold layers, Star Schema, Delta Tables, Delta Sharing, and analytical tables. • Collaborate with technical and business teams to orchestrate solutions aligned with company objectives. • Manage the technical roadmap for data projects, considering dependencies, trade-offs, and change management.

• Build, scale, and maintain robust data solutions. • Implement and optimize high-performance data pipelines: extraction, loading, transformation, and orchestration – that are designed for scalability, reliability, maintainability, and speed. • Champion modern software engineering practices as CI/CD, infrastructure-as-code, containerization, and cloud-native deployments • Collaborate closely with business stakeholders to transform use cases into production-ready services and solutions, owning the system from concept to production. • Implement rigorous testing and monitoring practices to maintain superior data quality and integrity.