Data Engineer

• Conduct comprehensive gap analysis and data mapping across 80+ ERP systems to identify integration challenges, data inconsistencies, and transformation requirements.
• Design and develop standardized ETL pipelines and data transformation processes for migrating enterprise data into Microsoft Fabric.
• Build robust data quality frameworks and validation rules to ensure data readiness for AI and analytics workloads.
• Lead hands-on implementation of ETL standards and best practices, establishing repeatable patterns and automation scripts for multi-source integrations.
• Develop and optimize data models that support seamless transformation from fragmented ERP sources into a unified, AI-ready data architecture.
• Lead the pilot implementation phase, testing and refining standards against real ERP data before full-scale rollout.
• Mentor and guide team members on ETL development, data transformation techniques, and Fabric-specific engineering practices.
• Collaborate with stakeholders to document data lineage, transformation logic, and integration patterns for knowledge transfer and governance.
• Troubleshoot data inconsistencies and implement corrective measures to maintain data integrity throughout the pipeline.

Lead Data Engineer

• Design and evolve enterprise data architecture strategies, including data models, data lakes, and data warehouses
• Lead the design and implementation of ETL/ELT pipelines to support analytics, reporting, and AI/ML workloads
• Architect and implement Master Data Management (MDM) frameworks to ensure data quality, consistency, and governance across systems
• Design and build modern data platforms using Databricks and Microsoft Fabric
• Architect cloud-native data solutions on AWS, ensuring scalability, security, and cost efficiency
• Define data governance standards, metadata management practices, and data lineage frameworks
• Collaborate with data science and AI teams to design data architectures that support machine learning, GenAI, and agentic system use cases
• Partner with business and technical stakeholders to translate data requirements into scalable architectural solutions
• Evaluate and recommend tools, technologies, and best practices for data integration, storage, and processing
• Provide technical leadership and mentorship to data engineering teams
• Troubleshoot complex data pipeline and platform issues, driving root cause analysis and long-term fixes
• Drive continuous improvement in data architecture, performance, and observability

Senior Data Architect

• Lead and mentor a team of data engineers, fostering best practices in coding, architecture, and data engineering standards.
• Define and drive the technical strategy for Journey Analytics data platforms, ensuring scalability, maintainability, and performance.
• Oversee the maintenance, optimization, and automation of code repositories in GitHub, ensuring high-quality and consistent development practices.
• Guide the refactoring of legacy codebases to improve maintainability, scalability, and reusability across multiple use cases.
• Drive the design and implementation of modular, reusable data components to support multiple journeys and reduce duplication.
• Oversee the development and management of automated data pipelines in Databricks, ensuring reliability and scalability for downstream consumption.
• Establish and enforce standards for scalable data modeling to support current and future analytics use cases.
• Ensure data quality, governance, performance, and reliability across all data pipelines and datasets.
• Partner with analytics, product, and engineering stakeholders to align data solutions with business needs and priorities.
• Proactively identify risks, bottlenecks, and improvement opportunities, and drive mitigation strategies at a team and platform level.
• Promote continuous improvement of data processes, documentation, and engineering practices.

Data Engineer Manager

• Partner with cross-functional teams to define BI and analytics requirements
• Innovate with new tools to improve Mutt Data's infrastructure and processes.
• Design dimensional data models and build scalable data pipelines
• Develop data models to feed dashboards for teams across the org, as well as client-facing analytics
• Develop tools for the team and assist in tech migrations and model design.
• Build scalable, high-performance data architectures.
• Focus on code quality—review, document, test, and integrate CI/CD.

• Own data pipeline design and implementation; consolidate 250+ transactional tables into ~10 analytic tables; move from weekly full-refresh to daily/near-real-time incremental loads via CDC (e.g., AWS DMS). Build and own the centralized analytics layer.
• Establish unified data definitions, metrics, KPIs, and benchmarks across the organization. Deliver a Master Data Management (MDM) framework with unified definitions and governance standards.
• Drive migration to the centralized layer while deprecating direct raw table access.
• Partner with internal departments to drive self-service analytics and build aggregations/data marts for benchmarking and experimentation.
• Conduct exploratory and statistical analysis for data validation, and implement data observability/monitoring.
• Partner with the Applied AI Engineer to design AI-ready data models (including retrieval- and embedding-ready structures to ground RAG and LLM applications). Expand the data lake to ingest third-party sources (e.g., HubSpot, Jira, Zendesk).

Senior Data Engineer

• Inventory and document all existing Talend jobs, transformation logic, and reconciliation rules.
• Operate the Datafold Migration Agent for automated bulk migration where supported.
• Manually convert ETL jobs that cannot be automated and validate the correctness of each.
• Pair-program with AI tooling to accelerate conversion — reviewing, fixing, and validating AI-generated output.
• Build DBT models and Snowpark functions to replace legacy Talend jobs.
• Implement automated data validation that surfaces quality and completeness issues via a Streamlit UI dashboard.
• Build a diagnostic tool to test the health of the full software stack and data connectivity end-to-end.
• Document all ETL logic changes throughout the migration.
• Maintain up-to-date data lineage documentation for the full pipeline

• Design and build scalable data pipelines to ingest, transform, and curate data from APIs, databases, files, and event streams.
• Lead technical design reviews and translate complex business needs into enterprise-grade data solutions.
• Develop and optimize advanced data models (dimensional, data vault, domain-driven, canonical) to support analytics, BI, and productized datasets.
• Champion SDLC best practices, continuous delivery, and infrastructure automation using CI/CD and Infrastructure as Code.
• Optimize complex distributed workloads using SQL, Python; mentor others on tuning and scalable design patterns.
• Build reusable data frameworks, libraries, and reference architectures to accelerate team productivity and platform adoption.
• Perform root-cause analysis for major data incidents, lead long-term remediation, and drive operational reliability improvements.
• Provide technical mentorship, guide code reviews, and help shape engineering capability maturity.
• Collaborate with Architects, Data Leads, Product Owners, and cross-functional engineering teams to define long-term data strategies.
• Perform other duties as assigned.

• Design and build clean data wherehouse and data processing.
• Participate in the entire development process, from designing small components to independently completing user stories.
• Lead and contribute to requirement analysis, planning, design, development and testing.
• Break down complex problems into manageable tasks and contribute to well-architected solutions.
• Collaborate with peers and seniors within your team and across the organization.
• Work with product managers using agile methodologies to deliver high-quality solutions on time.
• Partner with operations teams to ensure applications and services are highly available and reliable.
• Understand and address engineering-related concerns such as performance, network utilization, and resource impact.

• Construir y mantener pipelines de datos en Databricks que alimenten productos analíticos y aplicaciones del equipo DPI LATAM.
• Diseñar, desarrollar y mantener pipelines ETL/ELT en Databricks usando PySpark y SQL.
• Construir y optimizar tablas en Delta Lake siguiendo buenas prácticas de modelado (medallion architecture: bronze/silver/gold).
• Conectar e ingestar datos desde múltiples fuentes (SAP, archivos planos, APIs, bases relacionales).
• Asegurar calidad de datos: implementar validaciones, monitoreo y alertas básicas sobre los pipelines.
• Documentar los pipelines, diccionarios de datos y linaje de forma clara y mantenible.
• Colaborar con el equipo de analytics y el desarrollador de aplicaciones para definir los productos de datos necesarios.
• Utilizar herramientas de AI-assisted development (Claude Code, GitHub Copilot u equivalentes) para acelerar el desarrollo y la resolución de problemas.
• Participar en code reviews y mantener estándares de código limpio y reproducible.

Junior Data Engineer

• Own and evolve our data infrastructure—from FHIR ingestion through AI-powered enrichment.
• Work on one of our key projects with a U.S.-based partner company in the Health industry.
• Contribute to the modernization of our platform as we move from batch ETL to event-driven pipelines.
• Build and maintain pipelines ingesting FHIR data from customer EHRs into AWS HealthLake.
• Develop ETL processes using Glue/Spark for our multi-tenant Redshift warehouse.
• Lead our transition from batch Glue jobs to event-driven pipelines (Kinesis/Kafka).
• Build AI Agents using MCPs for data enrichment workflows pushing to HealthLake, OpenSearch, RDS.
• Improve our RAG pipeline with Neptune and S3 vector store integration.

Data & Software Engineer – Advanced English

• Architect and scale the data infrastructure behind our Machine Learning platform
• Design the systems that turn billions of data points into production-ready models
• Architect and scale advanced ETL pipelines using modern Big Data technologies
• Design resilient data frameworks across the full lifecycle: extraction → transformation → ML modeling
• Lead the development of Airflow-driven data workflows
• Optimize large-scale datasets and complex SQL queries
• Operationalize machine learning models in batch and real-time environments
• Improve cost-efficiency and scalability across our AWS data ecosystem
• Elevate data quality standards with strong governance and monitoring systems
• Build internal data tools that empower Marketing Science & Analytics teams

Senior Data Engineer – BI

• Design, develop, and maintain scalable ETL/ELT pipelines to process large volumes of data from diverse sources.
• Build and optimize data storage solutions — data lakes and data warehouses — for efficient retrieval and processing.
• Integrate structured and unstructured data from internal and external systems into a unified view for analysis.
• Ensure data accuracy, consistency, and completeness through validation, cleansing, and transformation.
• Maintain clear documentation for data processes, tools, and systems.
• Build and maintain Tableau dashboards and reports that translate complex datasets into clear, decision-ready visuals.
• Design data models and extracts optimized for Tableau performance, including live connections and published data sources.
• Apply data visualization best practices — chart selection, layout, color, and interactivity — to produce client-ready output.
• Partner with stakeholders to understand reporting needs and translate them into visual solutions.
• Support ad hoc analysis using Tableau, Python-based charting (matplotlib, seaborn, plotly), or similar tools.
• Support AI/ML workflows by building and maintaining the data pipelines that feed model training, inference, and evaluation.
• Assist with data preparation for LLM and machine learning projects, including feature engineering, tokenization pipelines, and vector store integration.
• Help teams adopt AI-assisted data tooling — copilots, intelligent search, automated reporting — by ensuring clean, well-structured data is available upstream.
• Contribute to prompt engineering and evaluation frameworks where data context is a key input.
• Work with product managers and stakeholders to gather requirements and translate them into technical solutions.
• Provide technical input during requirements sessions to align data capabilities with business needs.
• Participate in sprint planning, stand-ups, and sprint reviews.
• Deliver solutions on time and within scope. Adapt when priorities shift.
• Write unit and integration tests to validate pipeline reliability and data accuracy.
• Identify and resolve defects, performance bottlenecks, and data quality issues.
• Stay current with cloud platforms (AWS, Azure, GCP) and emerging data engineering tools.
• Propose solutions to improve performance, security, and scalability.

Data Engineer, Tableau

• Build and own the historical email ingestion pipeline via Microsoft Graph API
• Implement SharePoint / OneDrive document ingestion pipeline with scoped folder access
• Design and implement the PII minimisation pre-processing layer
• Build the vector store indexing workflow (OpenSearch/Pinecone) with per-tenant data isolation
• Define and implement the data processing schema; produce and maintain schema documentation
• Build the OCR routing orchestrator and integrate OCR service for scanned documents
• Implement the raw text / content extraction layer for all supported document types
• Define and prototype push vs. pull ingestion strategy, from one-time PoC through to incremental nightly pipeline
• Ensure data lineage and audit traceability are built into pipeline outputs from the outset

Senior Data Engineer, AI Ingestion Platform

• Develop and maintain SQL code and SSIS packages.
• Analyze data and solve new and existing business issues.
• Reviewing query performance and optimizing code.
• Provide production level support.
• Fully document all processes that are being created.

Prod Support Data Engineer, T-SQL

• Diseñar, desarrollar y mantener pipelines de datos robustos y escalables.
• Implementar procesos ETL/ELT para la integración y transformación de datos provenientes de múltiples fuentes.
• Desarrollar soluciones utilizando Databricks y Apache Spark.
• Construir y optimizar arquitecturas Lakehouse y Data Lake.
• Garantizar la calidad, consistencia y disponibilidad de los datos.
• Trabajar en conjunto con equipos de Data Analytics, BI, Data Science y negocio.
• Participar en iniciativas de optimización de performance, monitoreo y gobernanza de datos.
• Colaborar en el diseño de soluciones cloud alineadas con las mejores prácticas de ingeniería de datos.
• Documentar desarrollos, procesos y soluciones implementadas.

Ingeniero de Datos, Databricks

• Liderar y ejecutar el desarrollo de pipelines de datos en proyectos de analytics, con una visión integral de arquitectura de datos end to end.
• Colaborar estrechamente con el líder de equipo y el equipo de implementación, entregando visibilidad técnica del avance de las iniciativas.
• Apoyar en la definición, estimación y planificación de tareas para productos analíticos bajo metodologías ágiles.
• Liderar la gobernanza técnica de la solución junto al cliente, participando en decisiones clave.
• Diseñar e implementar arquitecturas de soluciones de datos en la nube de forma colaborativa.
• Participar en el modelado de datos y desarrollo de modelos analíticos corporativos.
• Investigar y profundizar en tecnologías necesarias para alcanzar los objetivos del proyecto.

• Support the automation, development, maintenance, and optimization of our data pipelines and infrastructure.
• Work closely with senior data engineers to process, clean, and transform large datasets, ensuring data integrity and availability for analytics and business decision-making.
• Collaborate with cross-functional teams to understand data needs and contribute to improving data workflows.
• Develop and maintain Python scripts and applications for data processing, analysis, and automation.
• Design, develop, and maintain Power BI dashboards and reports to deliver actionable insights to stakeholders.
• Reviewing data for accuracy, completeness and consistency
• Organizing and maintaining electronic records for a company’s databases
• Analyzing data in order to identify trends and opportunities for improvement
• Assisting in creating reports and proposals to present findings to management
• Address technical issues and provide IT support to ensure smooth business operations
• Support IT management team

• Diseñar y operar la estrategia de bases de datos productivas, asegurando performance, disponibilidad y escalabilidad.
• Llevar la operación a un modelo cloud-native sobre AWS (EKS, RDS), automatizando lo que hoy es manual.
• Implementar observabilidad, SLOs y prácticas SRE aplicadas a datos.
• Liderar proyectos de evolución del stack: upgrades de motor, migraciones a cloud, optimización de esquemas multi-tenant.
• Definir e implementar estrategias de backup, recovery y disaster recovery.
• Trabajar codo a codo con desarrollo en query design, schema migrations y revisión de cambios antes de que lleguen a producción.
• Automatizar tareas operativas con Bash, Python e infraestructura como código.
• Ser el referente técnico de datos en la empresa: documentar, definir estándares, mentorear.

Data Engineer – AWS

• Serve as the technical lead for the project—owning solution design decisions, guiding implementation standards, and mentoring other engineers through coaching, reviews, and knowledge sharing.
• Design, develop, and maintain complex ETL pipelines using Databricks, ensuring scalable, high-performance data integration across multiple source systems.
• Implement and optimize medallion architecture within Databricks, establishing clear data zones (raw, curated, trusted) to support governed, enterprise-wide reporting.
• Develop and refine dimensional data models that enable unified, analytics-ready views of business domains and support automated dashboarding and KPI frameworks.
• Collaborate closely with cross-functional teams (data stewards, IT, business stakeholders) to translate operational requirements into technical solutions, proactively clarifying dependencies and driving alignment.
• Contribute to architectural decisions, leveraging your expertise to recommend best practices, challenge assumptions, and ensure data platform durability and flexibility.
• Identify and address integration challenges, data quality issues, and process bottlenecks early, providing actionable insights and thoughtfully pushing back when project risks or inefficiencies arise.
• Support knowledge transfer and documentation, empowering colleagues and clients to maintain and evolve data solutions independently.

Lead Data Engineer – Databricks

• 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 jobs in Argentina

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Lead Data Engineer

Senior Data Architect

Data Engineer Manager

Lead Data Engineer

Senior Data Engineer

Data Engineer

Senior Data Engineer

Senior Data Engineer

Junior Data Engineer

Data & Software Engineer – Advanced English

Senior Data Engineer – BI

Data Engineer, Tableau

Senior Data Engineer, AI Ingestion Platform

Prod Support Data Engineer, T-SQL

Ingeniero de Datos, Databricks

Data Engineer

Junior Data Engineer

Data Engineer – AWS

Lead Data Engineer – Databricks

Data Engineer – Mid-Level