Senior Data Engineer (Senior L1)
Sobre el proyecto y el rol
Tipo de contrato: 100% Full Time Employee, contrato a término indefinido Modalidad: Si se encuentra a 50KM del Site de CDMX o del Site de Guadalajara de Globant, deben ir a la oficina 1 vez por semana, si se encuentran ubicados a 50KM del site de Monterrey, deben ir 2 veces por semana, si se encuentran fuera de estas locations, no requieren la modalidad Híbrida. Ubicación CDMX: Av. Insurgentes Sur 1271, Extremadura Insurgentes, Benito Juárez, 03740 Ciudad de México, CDMX, México Ubicación Guadalajara: Torre Andares Corporativo Paseo, Boulevard Puerta de Hierro 5153, Pisos 23 y 24, CP 45116 Ubicación Monterrey: Edificio Punto Valle, Calle Rio Missouri 555 Colonia del Valle, San Pedro Garza Garcia lobby, Supermanzana oriente piso 7, 66220 Monterrey, N.L., Mexico Horarios: lunes a viernes de 9 a 6pm México Inglés B2 o C1 Data Engineer (Senior L1) 1. Job Summary We are looking for a Senior Data Engineer (Senior L1) with strong technical judgment and the ability to understand the impact of every architectural and structural decision. The ideal candidate combines hands-on expertise in Python development and advanced SQL, design of analytical pipelines under Medallion architecture and Snowflake, and excellent communication skills to collaborate with stakeholders and multidisciplinary teams. 2. Technical Requirements (Hard Skills) Python (Intermediate/Advanced Level): ○ Deep understanding of data structures (strategic selection of dictionaries for JSON serialization/deserialization). ○ Conceptual and practical management of generators for efficient processing of data streams. ○ Use of decorators to extend functionality without overriding internal logic. ○ Dynamic query parameterization via argument unpacking (**kwargs). ○ Consumption and integration of external APIs using the requests library. Advanced SQL and Data Modeling: ○ Mastery of Window Functions, analytical query optimization, and performance differentiation between CTEs and Temporary Tables. ○ Design and classification of tables: Dimensions (DIM), Facts (FACT), and Log Tables. ○ Normalization and denormalization strategies according to the analytical use case. ○ Implementation of Slowly Changing Dimensions (SCD), with emphasis on SCD Type 2 (effective date management for auditing without overwriting). Data Architecture and Processing: ○ Implementation of Medallion Architecture (processing through D1 / Bronze, D2A / Silver, and D2B / Gold layers). ○ ELT vs. ETL Strategies: local pipelines (ETL) and native ELT orchestration in Snowflake (management and loading via stages). ○ Change Data Capture (CDC): log-based reading and streaming to topics (Kafka/event streams). ○ File formats and compression: analytical selection of Parquet with Snappy (CPU/I-O optimization) versus Avro for Kafka payloads. ○ Large-scale processing: PySpark fundamentals for volumes exceeding 10 million records. Infrastructure, Cloud, and DevOps: ○ Cloud & Containers: Configuration and deployment of Docker containers on AWS ECS (Fargate) without requiring direct EC2 instance provisioning; low-level metric monitoring and reading. CI/CD & Artifacts: Interpretation and modification of pipeline files in Jenkins (stage structuring) and package/dependency management in JFrog Artifactory. ○ Version Control (Git): Workflows, fast commit identification, and safe rollback procedures. Governance, Data Quality, and Testing: ○ Access management, roles, and governance: protection policies for sensitive data (access differentiation between direct base tables and controlled views). ○ Data quality: impact evaluation of null values on downstream analysis, imputation strategies, and metadata validation. ○ Data cataloging and transparent sharing through centralized metadata catalogs. ○ Software testing: clear design and distinction between unit testing and integration testing. 3. Soft Skills and Productivity English Proficiency: B2+ to C1 (proven ability to negotiate, document, and present technical ideas fluently in English). Stakeholder Communication: Clear reasoning and ability to translate complex technical concepts into business language. Blocker & Incident Management: Timely, proactive, and structured communication during technical impediments (clear reporting of progress, issues, and resolution paths). Cross-Functional Collaboration: Efficient and proactive coordination with other technical and product teams. Generative AI Adoption: Good judgment in leveraging AI development assistants (GitHub Copilot, Claude) and effective technical prompt engineering to accelerate code deliverables.