Data Engineer

• Diseñar, construir y optimizar arquitecturas de datos basadas en Azure y Databricks
• Implementar procesos ETL y ELT para integrar múltiples fuentes de información
• Diseñar y administrar arquitecturas Data Lake y Lakehouse
• Monitorear pipelines de datos para garantizar consistencia, calidad, trazabilidad y seguridad
• Preparar y optimizar datasets para analítica avanzada, reporting y modelos de Machine Learning
• Colaborar con equipos de BI, analítica y ciencia de datos para facilitar el acceso a la información
• Definir e implementar controles de gobierno y calidad de datos
• Implementar procesos de integración continua y despliegue continuo para soluciones de datos
• Diseñar soluciones escalables para procesamiento distribuido y grandes volúmenes de datos
• Identificar oportunidades de mejora para optimizar costos, rendimiento y tiempos de procesamiento
• Evaluar nuevas tecnologías y mejores prácticas para la evolución de la plataforma de datos

Ingeniero de Datos Azure – Databricks

• Support a broad set of architectural needs across multiple workstreams
• Design and own the data layer behind GenAI, RAG, and Agentic AI applications
• Ensure enterprise data and knowledge are ingested, prepared, stored, governed, and made available for AI consumption and retrieval
• Own and design data architecture, data modeling, ingestion, and platform modernization for enterprise Azure environments
• Design and implement scalable, metadata/configuration-driven data ingestion and data platform solutions
• Work with structured and genuinely unstructured data across the enterprise platform
• Modernize existing data platforms and ensure implementation-ready architecture

Senior Azure Data Architect – GenAI, RAG, Agentic AI

• Design module content for the Fintech Engineering Specialization at Jala University
• Create labs
• Develop a reference implementation
• Build an evaluation harness
• Prepare an instructor guide

Academic Content Developer – Financial Data Engineering, Credit Systems

• Define and communicate the long-term data architecture vision aligned with business strategy
• Lead evaluation and selection of technologies, frameworks, and standards for data architecture
• Establish and promote data management and governance best practices
• Collaborate with leadership teams and stakeholders to align data architecture with business needs
• Explain technical concepts in accessible terms
• Identify and solve complex technical challenges
• Provide advanced technical guidance to data development and architecture teams
• Ensure compliance with data security regulations and standards
• Mentor and empower team members
• Participate in defining and executing the organization's data strategy
• Develop and maintain strategic relationships with technology partners and suppliers
• Represent data architecture in internal and external technical and strategic forums
• Improve data system reliability and cross-team collaboration

Senior Data Architect

• Own the inventory of every credit data source, including TransUnion, Clarity, Plaid, Prism, and internal data from the app and loan servicing
• Define required data, grain, freshness, and destination for each source
• Write data contracts with engineers building vendor integrations
• Confirm raw vendor responses are stored completely for model development and audits
• Monitor freshness, volume, fill rate, and schema for every source and model input
• Set up alerts for feature fill-rate drops, missing vendor fields, and volume deviations
• Track input drift for production models alongside data scientists
• Triage data incidents, identify root causes, route fixes, and track closure
• Own the Credit Risk layer of the Snowflake warehouse, including raw, staging, mart, and feature tables
• Build and maintain pipelines and transformations for application, loan, performance, and vendor data
• Keep modeling datasets reproducible and prevent training-data leakage
• Maintain tables behind Credit Risk dashboards and governed metrics
• Write automated tests for keys, duplicates, ranges, referential integrity, and source reconciliation
• Maintain documentation, lineage, and owner registries
• Support vendor oversight by checking SLAs and reconciling pull counts against vendor invoices

Credit Risk Data Engineer

• Design, build, and maintain the core data foundation, including ingestion, modeling, and data marts
• Build ingestion resilient to API changes, deprecated fields, rate limits, and retroactive conversion restatements
• Model data across Meta, Google, TikTok, Amazon, LinkedIn, Microsoft, Shopify, Klaviyo, GA4, and client CRMs
• Contribute to client-bespoke modeling and client-specific data marts while extending the shared foundation
• Build semantic layers and metric definitions for consistent AI-generated SQL results
• Use AI-agentic workflows and AI coding tools to accelerate development and build intelligent data infrastructure
• Collaborate with nova product and engineering, AI/innovation, Client Service, BI, Tagging & Tracking, Data Ops, and client teams
• Monitor and resolve data quality issues and optimize pipelines for cost and performance across a multi-client warehouse
• Build and ship end-to-end data systems and production-ready datasets and pipelines supporting AI, product, agency, and client teams
• Reduce data fragmentation by building unified, AI-ready data foundations

Data Engineer, AI & Analytics

• Realizar la parametrización técnica y funcional de Salesforce
• Planificar e implementar procesos y ventanas de ejecución
• Desplegar y realizar QA
• Desarrollar componentes con Aura y Lightning Web Components
• Consumir y desarrollar servicios REST
• Desarrollar en Java, JavaScript y APEX
• Monitorizar procesos y atender alertas
• Participar en la adopción y evolución de componentes según el modelo de objetos de Salesforce
• Integrarse en equipos multidisciplinares de BBVA para impulsar la transformación digital y la innovación tecnológica

Data Engineering Associate II – Salesforce, AI Transformation

• Read and understand the existing codebase, specifications, READMEs, and migration history before making changes
• Diagnose and fix production failures, especially API synchronizations that break when vendor APIs change
• Review AI-generated pull requests line by line; test, challenge, and correct them
• Build CI/CD, automated tests, and monitoring so failures are caught early and never silent
• Harden database security, access control, and secrets management
• Build new features, integrations, data pipelines, and reports

Software Data Engineer

• Own and evolve the core data ingestion and transformation platform
• Design, deploy, and monitor mission-critical data pipelines with high availability and strict SLO/SLI performance
• Lead migration to a unified ingestion framework with standardized CDC patterns, schema evolution, and automated data quality checks across Bronze, Silver, and Gold layers
• Design monitoring dashboards, alerting systems, and runbooks for self-serve incident diagnosis and resolution
• Enforce enterprise-wide data governance, RBAC, and data classification for security, auditability, and compliance
• Create standard pipeline templates, CI/CD automated checks, and local testing utilities to improve developer productivity
• Collaborate with Product, Analytics, Finance, and Operations to translate business needs into robust, accessible datasets
• Optimize pipeline efficiency, automate manual processes, and introduce data engineering best practices
• Support reliable real-time financial decisions for Risk, Lending, and Finance

• Design, build, and own ETL/ELT pipelines bringing data from APIs, SaaS tools, CRMs, and transactional databases into Snowflake
• Model Snowflake data to make it clean, trustworthy, and usable for analytics and product
• Build and maintain integrations across the stack, selecting off-the-shelf connectors or custom code as appropriate
• Work directly with product and business leaders to turn vague questions into concrete data solutions
• Use AI tools and agents to accelerate work and automate manual team workflows
• Own work end to end: identify problems, propose solutions, ship them, and continuously improve them
• Create the data foundation behind a US startup partner's product and business decisions

Data Engineer jobs in Colombia