Data Engineer

• Design and operate the cloud data infrastructure powering AI initiatives.
• Architect production-scale data lakes on AWS.
• Build real-time ingestion and observability pipelines.
• Own the vector search and embedding layers that feed RAG systems and autonomous agents.

Senior Data Engineer – AWS, RAG Pipelines

• Define el modelo de datos transversal del producto, la estrategia analítica y las reglas de gobierno para todo el ecosistema.

Arquitecto de Datos

• Data Ingestion & ETL: Build Python/SQL pipelines to ingest invoices, orders, and catalogs.
• Perform historical backfills via SFTP/API and manage Airflow DAGs.
• Instance Configuration: Set up custom fields, product filtering logic, and sales workflows.
• Manage SSO and user provisioning for new rollouts.
• AI-Augmented Engineering: Leverage AI coding assistants (Copilot, Cursor) and LLMs to accelerate Python/SQL script generation, data mapping, and debugging.
• Customer Communication & Projects: Act as a technical point of contact. Translate complex data issues into clear updates for customers. Own project milestones from kickoff to "Go Live."
• Integration & Automation: Build Workato recipes and connect customer ERPs via APIs/webhooks to ensure real-time data flow.
• QA & Troubleshooting: Triage HubSpot support tickets, debug data discrepancies in large data sets, and deploy production fixes.
• Documentation: Maintain customer data mappings and internal technical runbooks.

• Diseñar y desarrollar soluciones de datos utilizando herramientas modernas.
• Colaborar con equipos multidisciplinarios para definir requisitos y expectativas del cliente.
• Implementar prácticas de ingeniería de datos para optimizar el rendimiento de las aplicaciones.

Senior Data Engineer

• Encargado de extraer postings, eventos y balances de Vault y replicarlos al almacenamiento analítico y operacional.
• Construir los pipelines de datos que salen del core (Vault): extracción de postings, eventos y balances; replicación a almacenamiento analítico y operacional.

Ingeniero de Datos Senior

• Construir los pipelines de datos de los satélites y consolidar el dato de todo el producto en el modelo analítico definido por el Arquitecto de Datos.

Semi-Senior Data Engineer

• Business Context Apoyar al equipo de Sostenibilidad de LATAM Airlines en la evolución de su plataforma de datos. El proyecto consiste en integrar y modelar distintas fuentes corporativas junto con la Base de Datos de Sostenibilidad, habilitando información confiable, trazable y escalable para análisis, reportabilidad y toma de decisiones.

• Responsible for expanding and optimizing our data and data pipeline architecture.
• Optimizing data flow and collection for cross functional teams.
• Support our product owners, product analysts, data analysts and data scientists on data initiatives.
• Ensure optimal data delivery architecture is consistent throughout ongoing work.
• Engage with business stakeholders to establish clear needs and links to solutions, including setting up prototypes, and involving multiple parties in design sessions.
• Create data collection, extraction, and transformation frameworks for structured and unstructured data.
• Develop and maintain data infrastructure systems (e.g. data warehouses) including data access points.
• Prepare and manipulate data using a variety of data integration and data pipeline tools including, but not limited to, Talend, SQL, Snowflake, Kafka, Azure Data Factory.
• Create efficient load processes including logging and handling of exceptions, notification to support, and overall visibility operations.
• Organize data into formats and structures that optimize reuse and efficient delivery to businesses and analytics teams and system applications.
• Integrate data across data lake, data warehouse and systems applications to ensure the consistent delivery of information across the enterprise.
• Accountable for efficient architecture and systems design.
• Build and evolve the data service layer and engages the team to bring together components for a best-in-class customer offering.

Senior Data Engineer – Snowflake

• Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes
• Build and optimize scalable data pipelines supporting batch and real-time processing
• Define and enforce data governance, quality standards, and compliance frameworks across the platform
• Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation
• Drive data platform modernization, optimizing for performance, cost, and scalability
• Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace
• Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams
• Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows
• Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption
• Partner with leadership on data strategy, translating technical depth into decisions others can act on
• Collaborate closely with engineering, analytics, AI, and product teams to align data platforms with broader goals
• Advocate for data quality, governance, and platform best practices across teams and engagements
• Establish data engineering standards that lift the quality and consistency of work across the team
• Mentor junior and mid-level engineers, helping them grow their craft, confidence, and impact
• Make high-stakes architectural decisions with clear ownership and consideration of long-term tradeoffs

Staff Data Engineer

• Diseñar, desarrollar y mantener soluciones de integración y procesamiento de datos en el ecosistema Azure.
• Asegurar la disponibilidad, calidad y trazabilidad de la información mediante la construcción y orquestación de pipelines de datos escalables.
• Orquestar procesos de integración de datos desde múltiples fuentes, incluyendo bases de datos relacionales, sistemas on-premise, APIs y servicios cloud.
• Configurar y administrar Linked Services, Integration Runtime y Self-hosted Integration Runtime.
• Implementar procesos de transformación y preparación de datos mediante Mapping Data Flows.
• Gestionar la carga y almacenamiento de datos en Azure Data Lake Storage Gen2, Azure Blob Storage, Azure SQL Database y Azure Synapse Analytics.
• Desarrollar consultas y procesos de transformación utilizando SQL y Python.
• Implementar mecanismos de control de versiones y despliegue continuo mediante Git y Azure DevOps.
• Aplicar buenas prácticas de seguridad, gobernanza y acceso a datos utilizando Key Vault, Managed Identities y RBAC.
• Monitorear y optimizar el rendimiento de los procesos de integración y carga de datos.
• Documentar desarrollos, flujos de integración y procedimientos operativos asociados a las soluciones implementadas.

• Design, build, and maintain reliable and scalable data pipelines, primarily leveraging SQL
• Develop and optimize complex queries to support analytics and reporting needs
• Ensure data accuracy, consistency, and quality across multiple data sources
• Partner with stakeholders to translate business questions into data solutions
• Support experimentation frameworks, including A/B testing and data validation
• Work with analytics teams to deliver datasets used in dashboards and reporting tools (e.g., Metabase or similar)
• Communicate insights effectively through data storytelling, tailoring messages for non-technical audiences
• Collaborate with cross-functional teams in a client-facing consulting environment
• Contribute to best practices in data modeling, documentation, and governance

Data Engineer – Mid-Level

• Aportar ideas y conocimientos en soluciones a las necesidades de las personas
• Extraer, transformar, conocer y desplegar datos
• Automatizar datos
• Contar con el acompañamiento de un mentor y cursos para fortalecer estos conocimientos

Ingeniero de Datos

• Transformar datos en decisiones estratégicas de alto impacto.
• Gestionar arquitecturas de datos complejas, garantizando que la información fluya con precisión y eficiencia hacia las capas de visualización.

Ingeniero de Datos – Teradata, MicroStrategy

• Build and scale data pipelines and supporting infrastructure on AWS
• Develop and implement data transformations, including: Design and configure AWS Glue jobs, Spark transformations, and Python-based ETL pipelines
• Write and maintain integration tests to validate data movement across raw → staged zones
• Implement data quality frameworks, including: Ensure data reliability, consistency, and performance across pipelines
• Collaborate with cross-functional teams to improve data architecture and processes
• Leverage AI-assisted development tools to generate boilerplate code (60–70%) and accelerate development cycles

• Build, maintain, and optimize scalable data solutions to support Journey Analytics initiatives.
• Maintain, optimize, and automate existing code repositories in GitHub.
• Refactor legacy code to simplify maintenance, updates, and reuse across multiple use cases.
• Design and build modular, reusable code components to support multiple journeys and reduce duplication.
• Develop and manage automated data pipelines in Databricks to support Journey Analytics datasets and downstream reporting.
• Consolidate key KPIs, metrics, and attributes into standardized data structures to enable flexible journey views.
• Build and maintain scalable data models to support current and future journey analytics use cases.
• Ensure data quality, performance, and reliability across data pipelines and analytics datasets.
• Collaborate with analytics and engineering teams to improve data processes and architecture.

• Administrar, monitorear y optimizar ambientes de bases de datos Oracle.
• Gestionar Oracle Database 19c, RAC, ASM, Grid Infrastructure y Data Guard.
• Realizar actividades de performance tuning, backup/recovery y troubleshooting.
• Administrar ambientes Oracle Cloud y Autonomous Database.
• Garantizar disponibilidad, integridad y seguridad de bases de datos empresariales.
• Trabajar junto a equipos de infraestructura y aplicaciones para soportar plataformas críticas.
• Documentar configuraciones, procedimientos y estándares operativos.
• Participar en proyectos de migración, actualización y transformación cloud

Ingeniero de Datos, Oracle

• Build and maintain ELT pipelines using Airbyte or Fivetran across APIs, files, and application databases.
• Implement comprehensive unit and integration tests (dbt tests, freshness checks) for every pipeline.
• Build and maintain the Bronze (Raw) and Silver (Cleaned) layers on Snowflake using strict data quality gates.
• Support the creation of Gold-layer Star Schemas and reporting models when capacity requires.
• Collaborate with the Architect to build the foundational tables that support downstream analysis and the Semantic Layer.
• Interpret legacy scripts to migrate logic into the new stack with verified parity.
• Actively participate in our hiring processes by serving on interview panels for future roles across the company.

• Consultor Ingeniero de Datos Azure
• Dataricks
• SQL
• Transformación de Datos

Consultor Ingeniero de Datos, Azure

• Lead the design and implementation of data ingestion architectures in Snowflake , ensuring scalability and reliability.
• Own the development of end-to-end ELT pipelines integrating multiple data sources.
• Design and enforce data quality frameworks , including validation rules, testing strategies, and monitoring.
• Apply and drive Medallion Architecture (Bronze, Silver, Gold) best practices across data pipelines.
• Build and optimize data pipelines and architectures on AWS (S3, Glue, Lambda, EMR, etc.).
• Develop and enhance data validation and ingestion frameworks , including UI components (Streamlit-based) when needed.
• Act as a technical leader , guiding best practices in data engineering, governance, and pipeline reliability.
• Proactively identify risks, bottlenecks, and data quality issues , and implement mitigation strategies before they impact delivery.
• Contribute to data governance initiatives , including data definitions, lineage, and stewardship practices.
• Drive documentation and continuous improvement of data platform processes.

Data Engineering Lead

• Design, build, and maintain data pipelines and ETL processes using Databricks and Apache Spark.
• Optimize data workflows for performance, scalability, and cost efficiency.
• Implement data Lakehouse architecture and manage data ingestion from multiple sources.
• Collaborate with data scientists and analysts to enable advanced analytics and machine learning workloads.
• Ensure data quality, governance, and security across all data assets.
• Monitor and troubleshoot Databricks clusters, jobs, and workflows.
• Integrate Databricks with cloud services (AWS, Azure, or GCP) and other enterprise systems.
• Document processes, standards, and best practices for data engineering.

See all jobs on JobTailor

Senior Data Engineer – AWS, RAG Pipelines

Arquitecto de Datos

Data Engineer

Senior Data Engineer

Ingeniero de Datos Senior

Semi-Senior Data Engineer

Senior Data Engineer

Senior Data Engineer – Snowflake

Staff Data Engineer

Data Engineer

Data Engineer – Mid-Level

Ingeniero de Datos

Ingeniero de Datos – Teradata, MicroStrategy

Data Engineer

Senior Data Engineer

Ingeniero de Datos, Oracle

Senior Data Engineer

Consultor Ingeniero de Datos, Azure

Data Engineering Lead

Lead Data Engineer