Data Engineer

• Lead cross-functional programs from planning to execution, ensuring alignment on scope, timelines, and business objectives.
• Define program governance, KPIs, and success metrics.
• Proactively identify risks, dependencies, and early signals to prevent issues before they impact delivery.
• Analyze enterprise data to identify trends, opportunities, and operational insights.
• Design and maintain scalable data models and workflows using Microsoft Dataverse.
• Ensure data quality, governance, security, and compliance.
• Develop and enhance PowerApps and Power Automate solutions to streamline business processes.
• Build Power BI dashboards and reports that enable data-driven decision-making.
• Translate business requirements into scalable technical solutions.
• Partner with Engineering, Product, and Business teams to deliver strategic initiatives.
• Communicate progress, risks, and outcomes effectively to leadership.

Technical Program Manager – Data & Power Platform

• Translate legacy ETL logic from SSIS and stored procedures into modern ELT pipelines using dbt
• Implement Data Vault 2.0 structures including Raw Vault and Business Vault
• Build datamarts and curated datasets for downstream analytics and reporting
• Design and operate workflows using Dagster, including scheduling, dependencies, and recovery mechanisms
• Deploy and run data workloads on OpenShift / Kubernetes environments
• Enable near real-time data processing using Kafka-triggered pipelines
• Integrate with upstream data lake environments and external data providers
• Establish robust data validation and reconciliation processes
• Implement automated testing and monitoring using dbt
• Support production pipelines and resolve incidents when required
• Create clear documentation and ensure operational readiness
• Continuously improve performance, reliability, and maintainability

Senior Data Engineer

• Assist in translating legacy ETL processes into modern ELT pipelines using dbt
• Support the development of Data Vault 2.0 models
• Build and maintain data transformations and curated datasets
• Contribute to data integration and migration activities
• Support workflow development using Dagster
• Assist in deploying and monitoring workloads on OpenShift / Kubernetes
• Participate in pipeline monitoring and operational support
• Help implement automated data validation and testing
• Support reconciliation between legacy and modern platforms
• Investigate and resolve data quality issues together with senior team members
• Document technical solutions and operational procedures
• Learn and apply software engineering best practices
• Contribute ideas for improving performance and maintainability
• Participate in code reviews and team knowledge sharing

Junior Data Engineer

• Design, develop, and maintain data pipelines to ingest, transform, and normalize data from multiple sources.
• Lead the design and implementation of data ingestion architectures in Snowflake, ensuring scalability and reliability.
• Apply best practices in data validation to ensure accuracy, consistency, and reliability across datasets.
• Perform data transformation and normalization to support analytics and reporting use cases.
• Work with large datasets using SQL and Python to process and analyze data efficiently.
• Ensure data quality by implementing validation rules, checks, and monitoring processes.
• Collaborate with business and technical stakeholders to understand data requirements and translate them into scalable solutions.
• Support ETL processes and file handling workflows for structured and semi-structured data.
• Contribute to documentation and continuous improvement of data processes and standards.

• Assisting in daily operations and projects within the Product Engineering area.
• Supporting data analysis, reporting, and documentation tasks.
• Collaborating with cross-functional teams to ensure timely execution of deliverables.
• Researching and preparing presentations or reports as needed.
• Contributing ideas to improve processes and enhance team efficiency
• Collect, map, and analyze data schemas and process flows required to convert data from competitor applications into our software suites.
• Utilize Generative AI and Large Language Models (LLMs) to automatically generate, refine, and maintain high-quality technical documentation of competitor processes.
• Collaborate closely with senior software engineers and data specialists to understand technical requirements and system architectures.
• Perform data exploration and profiling to identify patterns, mapping rules, and potential transformation bottlenecks.
• Verify the accuracy of LLM-generated documentation against actual database structures and codebases.
• Participate in agile ceremonies (daily stand-ups, sprint planning, and reviews) to share progress and learn best practices in software engineering.

Engineering Intern – Data & AI Automation

• Diseñar e implementar soluciones de CDC en tiempo real para integración y replicación de datos.
• Uso de GoldenGate for Big Data (19.x / 21.x, Kafka Handler, File/HDFS Handler).
• Uso de GoldenGate std 12, 19.x / 23.x.
• Linux/Unix (RHEL 7 / 8)
• Automatización con Shell / Python 3.x
• Monitoreo, troubleshooting y optimización de desempeño.
• Configuración de CDC (log-based) y validación de datos end-to-end.
• Configurar procesos de captura, transformación y entrega de datos.
• Analizar requerimientos de negocio para replicación (near real-time, batch híbrido).
• Validar consistencia de datos entre sistemas origen y destino.
• Instalar, configurar y administrar Oracle GoldenGate (OGG) en diversas plataformas.
• Configurar procesos clave: Extract, Pump (Data Pump) y Replicat.
• Implementar replicación unidireccional y bidireccional.
• Administrar trails files, checkpoints y parámetros avanzados.
• Monitorear latencias de replicación (lag).
• Optimizar performance de GoldenGate:
• Tuning de Extract y Replicat
• Configuración de batching y parallelism
• Diagnosticar y resolver issues de replicación en tiempo real.
• Análisis de errores en logs (discard, report files).
• Resolución de problemas de: data mismatch, conflictos en replicación bidireccional y DDL replication issues.
• Implementar cifrado de datos en tránsito (OGG encryption).
• Asegurar consistencia transaccional durante replicación.
• Configurar filtros y transformaciones seguras.
• Controlar acceso a procesos y archivos de replicación.
• Diseñar arquitecturas de Change Data Capture (CDC) orientadas a eventos usando Oracle GoldenGate + Apache Kafka.
• Implementar pipelines de datos near real-time hacia Kafka para consumo downstream.
• Definir estrategias de publicación de eventos (topic design, partitioning, keys).
• Alinear modelos de datos transaccionales con arquitecturas event-driven.
• Configurar Oracle GoldenGate for Big Data para publicación hacia Kafka.
• Implementar handlers de Kafka: Kafka Handler, JSON.
• Configurar procesos: Extract (captura de redo logs), Data Pump (transporte), Replicat con salida a Kafka topics.
• Manejar parámetros avanzados (MAP, COLMAP, FILTER, FORMAT).
• Validar consistencia entre Oracle y Kafka topics.
• Implementar reconciliación de eventos.
• Auditar flujos de datos en tiempo real.
• Gestionar schemas con Schema Registry.

• Diseñar, desarrollar e implementar pipelines de datos eficientes y escalables utilizando diversas tecnologías y herramientas.
• Colaborar con equipos de ciencia de datos, analistas y otras partes interesadas para comprender los requisitos de datos y traducirlos en soluciones técnicas.
• Optimizar la infraestructura de datos y los procesos ETL/ELT para mejorar el rendimiento y la fiabilidad.
• Garantizar la calidad, consistencia y seguridad de los datos a lo largo de todo el ciclo de vida.
• Participar en la evaluación y selección de nuevas tecnologías y herramientas de ingeniería de datos.
• Documentar soluciones técnicas, procesos y mejores prácticas.

• Design and implement robust, scalable data pipelines using Databricks, Apache Spark, and Delta Lake as well as BigQuery
• Design and implement efficient data pipeline frameworks, ensuring the smooth flow of data from various sources to data lakes, data warehouses, and analytical platforms
• Troubleshoot and resolve issues related to data processing, data quality, and data pipeline performance
• Document data infrastructure, data pipelines, and ETL processes, ensuring knowledge transfer and smooth handovers
• Create automated tests and integrate them into testing frameworks
• Configure and optimize Databricks workspaces, clusters, and job scheduling
• Work in a Multi-cloud environment including Azure, GCP and AWS
• Implement security best practices including access controls, encryption, and audit logging
• Build integrations with market data vendors, trading systems, and risk management platforms
• Establish monitoring and performance tuning for data pipeline health and efficiency
• Collaborate with cross-functional teams to understand data requirements, identify potential data sources, and define data ingestion
• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and deliver data solutions that meet their needs

• Designing and developing data infrastructure and systems to facilitate efficient data processing and analysis
• Implementing data pipelines, integration, and transformation solutions to support client needs
• Utilizing advanced technologies and techniques to convert raw data into actionable insights
• Collaborating with clients to understand their data requirements and deliver tailored solutions
• Managing and optimizing data architecture to enhance performance and scalability
• Leveraging tools such as Apache Airflow, Azure Data Factory, and Databricks for data engineering tasks
• Confirming data quality and integrity through validation and troubleshooting processes
• Building and maintaining data warehouses and lakes to support analytics and reporting
• Applying analytical thinking and problem-solving skills to address complex data challenges
• Mentoring junior team members and fostering a collaborative team environment

Senior Data Engineer – SSIS Enhancements

• Diseñar, desarrollar, implementar y ajustar sistemas y canalizaciones distribuidos a gran escala
• Procesar grandes volúmenes de datos, centrándose en escalabilidad, baja latencia y tolerancia a fallos
• Redactar consultas complejas y altamente optimizadas en grandes conjuntos de datos

• Define and drive the data engineering technical strategy, architecture decisions, and platform roadmap aligned to company objectives
• Lead and deliver large-scale, complex data initiatives—spanning multiple teams and iterations—from ambiguous problem definition through production deployment
• Design robust, scalable data architectures (batch and streaming) that support Kueski's long-term business needs at scale
• Demonstrated success shaping and executing an AI-centric data strategy that leverages the latest AI technologies to accelerate value delivery, enable trusted self-service data consumption, and strengthen data quality, governance, and organizational decision-making.
• Identify the limits of existing tools or processes; lead the design and build of new capabilities when current solutions fall short
• Shape, standardize, and champion data engineering methodologies, best practices, and technical standards for the team and department
• Develop and own CI/CD pipelines and infrastructure-as-code for reliable, automated data platform operations
• Drive data quality, observability, and governance programs across the data platform
• Apply data cleansing techniques to facilitate data consumption and quality across the platform
• Partner cross-functionally with Data Science, ML, Analytics, Platform, and Product teams to deliver data-driven solutions end-to-end
• Represent data engineering in cross-organizational initiatives; support and lead efforts outside the core area of responsibility
• Mentor and guide Data Engineers at all levels; constructively challenge assumptions and elevate team quality through code review, pairing, and coaching

Staff Data Engineer

• Coordinar los servicios y soporte de TI para usuarios internos.
• Dar seguimiento a proyectos tecnológicos liderados por la alta dirección.
• Brindar apoyo en temas de infraestructura tecnológica y ciberseguridad.
• Gestionar administrativamente el área de Tecnologías de la Información.
• Impulsar la adopción y aprovechamiento de herramientas tecnológicas dentro de la organización.
• Generar reportes, dashboards e indicadores para la toma de decisiones.
• Analizar información estratégica para identificar oportunidades de mejora.
• Coordinar actividades con equipos internos y áreas operativas.

Arquitecto de Datos

• Diseñar y desarrollar procesos ETL y Data Pipelines.
• Participar junto a equipos de negocio y tecnología en la definición de KPIs y dashboards.
• Construir soluciones de integración, almacenamiento y transformación de datos.
• Optimizar modelos de datos, consultas y estructuras para maximizar rendimiento y eficiencia.
• Diseñar arquitecturas y documentar soluciones utilizando UML y estándares de arquitectura.
• Implementar mecanismos de monitoreo, observabilidad y control de calidad de datos.
• Realizar pruebas técnicas y acompañar despliegues e iniciativas de mejora continua.
• Presentar avances y resultados a equipos técnicos y stakeholders de negocio.

• Partner with Product, Analytics and Engineering to build scalable systems that help unlock the value of data from a wide range of sources such as backend databases, event streams, and marketing platforms
• Lead technical vision and architecture with holistic point of view on both short-term and long-term horizons
• Work with analytics to create company wide alignment through standardized metrics across the company
• Work with Product and Engineering teams to support internal use cases such as financial reporting, product analytics and operational metrics
• Enable external use cases like customer-facing dashboard, self-serve analytics, and next best action in product
• Manage the complete data stack from ingestion through data consumption
• Build tools to increase transparency in reporting company wide business outcomes
• Work with DevOps to deploy and maintain data solutions leveraging cloud data technologies, preferably in AWS
• Help define data quality and data security framework to measure and monitor data quality across the enterprise. Define and promote data engineering best practice

Lead Data Engineer

• Work at DaCodes!We are a high-impact software and digital transformation firm.
• For over 10 years, we have developed technology and innovation-driven solutions thanks to our team of nearly 300 talented #DaCoders, including developers, architects, UX/UI designers, PMs, QA testers, and more. Our team collaborates on projects with clients across LATAM and the United States, delivering outstanding results.
• At DaCodes, you will have the opportunity to accelerate your professional growth, work on diverse projects across multiple industries, and contribute to the design, implementation, and optimization of cloud-based and data-driven solutions.
• Our DaCoders have a significant impact on the success of both our business and our clients. You will be the expert participating in our projects and will gain exposure to disruptive startups and global brands.
• Interested?
• 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.

Senior Data Engineer – Graph Databases, Databricks

• Plan and execute end-to-end data migrations, including extraction, cleaning, transformation, validation, loading, and reconciliation.
• Analyze source data, create field mappings, and define transformation rules for migration into or out of Legal Tracker.
• Prepare and validate CSV, Excel, XML, and other import/export file formats.
• Write SQL queries for data extraction, validation, and reconciliation across relational databases.
• Develop and execute test plans, perform mock migrations, and reconcile source and target system records.
• Troubleshoot migration errors, resolve data exceptions, and ensure complete and accurate conversion outcomes.
• Work with APIs, SFTP, middleware, and integration workflows to support automated data feeds and system-to-system transfers.
• Collaborate with legal operations, finance, IT, outside counsel, vendors, and implementation partners throughout the migration lifecycle.
• Maintain documentation to ensure secure, auditable, and compliant data migration processes.
• Develop strong knowledge of legal operations data, including matters, invoices, vendors, timekeepers, budgets, accruals, and related metadata.

ETL/Data Migration Engineer

• Diseñar e implementar arquitecturas de datos en Microsoft Fabric.
• Desarrollar pipelines de ingesta de datos.
• Conocimiento de modelos semánticos en Power BI conectados a Fabric.
• Conocimiento de modelos de datos.
• Conocimiento rendimiento de consultas DAX, SQL.

Ingeniero de Datos, Fabric PI

• - Design, build, and maintain scalable data pipelines that support analytics, reporting, and operational workflows.
• - Develop production-grade ETL/ELT pipelines using Python.
• - Orchestrate workflows using Apache Airflow.
• - Design, develop, and maintain data transformation models using dbt.
• - Build and maintain API-based integrations with internal and external systems.
• - Develop and optimize Snowflake data warehouse solutions to support analytical workloads.
• - Ensure data quality, reliability, monitoring, and observability across all data pipelines.
• - Optimize existing pipelines, transformations, and data models for performance and scalability.
• - Collaborate closely with Product, Analytics, and Engineering teams to translate business requirements into production-ready data solutions.
• - Troubleshoot pipeline failures, API integration issues, and data quality problems.
• - Document technical solutions, pipeline architecture, and operational best practices.
• - Support production environments and continuously improve platform reliability.

• Collaboration with U.S-based client stakeholders on solution design/definition. Identify solutions that solve business problems, translating requirements into technical specifications and actionable work.
• Writing code and implementing the proposed solutions
• Creating data pipelines, versioning and change management
• Manage the complexity inherent in versioned data pipelines
• Develop ETL/ELT processes to help extract and manipulate data from multiple sources.
• Design, build and maintain batch or real-time data pipelines in production.
• Automate data workflows such as data ingestion, aggregation, and ETL processing.
• Logging and instrumentation of pipelines and services.
• Ensure data accuracy, integrity, privacy, security, and compliance through quality control procedures.

Data Engineer jobs in Mexico

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Technical Program Manager – Data & Power Platform

Senior Data Engineer

Junior Data Engineer

Senior Data Engineer

Engineering Intern – Data & AI Automation

Data Engineer

Data Engineer

Senior Data Engineer

Senior Data Engineer – SSIS Enhancements

Data Engineer

Staff Data Engineer

Arquitecto de Datos

Data Engineer

Data Engineer

Lead Data Engineer

Senior Data Engineer – Graph Databases, Databricks

ETL/Data Migration Engineer

Ingeniero de Datos, Fabric PI

Senior Data Engineer

Senior Data Engineer, PowerBI, Data Integration