Data Engineer

• 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

Junior Data Engineer

• 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 – Mid-Level

• Design and implementation of scalable, domain-oriented data solutions for a high-impact enterprise initiative.
• Work within a specific data domain (Ingestion, Customer Data, Enrichment, or Marketing Signals).
• Ensure high-quality, reliable data pipelines that power analytics and business-critical workflows.
• Build and optimize batch and streaming ingestion pipelines, including CDC-based architectures.
• Support the design and implementation of data models aligned with business and analytics needs.
• Ensure data quality, reliability, and performance across pipelines and datasets.
• Collaborate with cross-functional teams to align technical solutions with business requirements.
• Apply best practices in data engineering, including modular design, reusability, and governance.
• Leverage AI-assisted development tools to improve productivity and code quality.
• Proactively identify risks, bottlenecks, and optimization opportunities across data workflows.

Senior Data Engineer

• Design, develop, and maintain scalable data pipelines using Azure Data Factory, SQL, and Python.
• Build and optimize ETL/ELT processes across Microsoft-based cloud data platforms.
• Work with Microsoft Fabric to support modern analytics and data engineering workflows.
• Develop and maintain data models and transformation logic to support reporting and analytics needs.
• Collaborate with analysts, business stakeholders, and technical teams to translate business requirements into technical solutions.
• Support Power BI reporting needs by enabling clean, reliable, and well-structured datasets.
• Ensure data quality, consistency, and performance across ingestion, transformation, and reporting layers.
• Monitor and optimize workflows for reliability, scalability, and maintainability.
• Contribute to CI/CD, deployment, and operational best practices within the Microsoft ecosystem.
• Support data governance, documentation, and process improvement efforts.

Data Engineer, Azure, SQL

• Identifying, creating, preparing data required for modern Data solutions and Data for AI.
• Designing ETLs and ELTs for data transformations.
• Designing, building and management of Datalakes architectures.
• Working with Apache Hadoop projects (Spark, Hive, Pig, Oozie, Airflow, etc).
• Integrating and testing Data solutions.
• Creating and documenting the tests to meet requirements.
• Working with Bigdata Environments.
• Managing Data Cloud services, data access, security and data governance.
• Analyzing and prepare data for Machine Learning workloads.
• Managing monitoring and logs of applications and services.

AWS Data Engineer

• Design and develop scalable data pipeline architectures for batch and real-time processing
• Build and maintain API-based ingestion frameworks and event-driven data streams
• Develop robust Python-based data processing workflows
• Integrate data from multiple sources
• Implement and optimize data models and transformations for downstream consumption
• Contribute to building AI-ready data infrastructure, including: Embedding pipelines, Vector database integrations
• Collaborate with analytics, BI, and AI teams to enable data accessibility and reuse
• Ensure data quality, monitoring, and reliability across pipelines
• Follow best practices for performance, scalability, and governance

Data Engineer – Pipelines, APIs, AI Data Systems

• Design, implement, and optimize data pipelines that extract, transform, and load data into Snowflake from multiple sources using Airflow and AWS services
• Build modular, well-documented dbt models with strong test coverage to serve business reporting, lifecycle marketing, and experimentation use cases
• Partner with analytics and business stakeholders to define source-to-target transformations and implement them in dbt
• Maintain and improve our orchestration layer (Airflow/Astronomer) to ensure reliability, visibility, and efficient dependency management
• Collaborate on data model design best practices, including dimensional modeling, naming conventions, and versioning strategies

Data Engineer – Data Pipelines, Modeling

• Lead the design, development, and optimization of data pipelines using Databricks (including Delta Live Tables / Lakeflow) on Azure.
• Migrate and refactor legacy stored procedures and sprawling MSSQL processes into modern, governed Databricks solutions.
• Contribute to data platform modernization initiatives, including medallion architecture, data quality, and governance practices.
• Collaborate with the client’s team and our Analytics Engineers to deliver high-quality, production-ready data assets.
• Work in a client-facing consulting environment: gather requirements, provide recommendations, and professionally push back when needed to ensure the best technical outcomes.
• Apply broad data engineering skills (pipelining, orchestration, cloud infrastructure) while deepening your expertise in the modern data stack.

• Build and maintain scalable, reliable, and secure data platforms
• Design and implement robust ETL/ELT pipelines for structured and unstructured data
• Ensure data quality, lineage, governance, and compliance across data platforms
• Collaborate with Data Science and AI teams to productionize machine learning pipelines
• Monitor and troubleshoot data workflows and system performance

• Design and implement the enterprise data architecture.
• Lead the development of scalable, secure, and integrated data platforms to support analytics, compliance, and business operations.
• Define and implement the company’s data architecture, models, and pipelines.
• Design integrations between internal platforms and third-party systems (Salesforce, CX platforms, etc.).
• Establish standards for data quality, metadata, lineage, and classification.
• Partner with engineering, security, and compliance teams to align on privacy, retention, and access control policies.
• Lead the selection and implementation of data platforms.
• Drive adoption of data best practices across teams and support data governance efforts.
• Maintain data platforms and architecture performance.
• Improve data models to support business growth and analytics.

Senior Data Architect, English Required

• Diseñar, desarrollar y mantener modelos de datos en BigQuery, aplicando particionado, clustering y optimización de costos y performance.
• Desarrollar y orquestar transformaciones utilizando Dataform (SQLX), implementando modelos incrementales, gestión de dependencias, estándares de naming y documentación.
• Construir pipelines y automatizaciones en Google Cloud Platform, incluyendo ingestas desde Cloud Storage hacia BigQuery y orquestaciones programadas.
• Desarrollar servicios y jobs en Python para integraciones, consumo de APIs, validaciones de datos, automatización de cargas y conectores internos.
• Empaquetar y desplegar servicios en Cloud Run (jobs o servicios HTTP), asegurando observabilidad mediante logs y métricas, y garantizando escalabilidad.
• Aplicar buenas prácticas de ingeniería de software: control de versiones con Git, revisiones de código, testing y procesos de CI/CD.
• Colaborar con equipos de negocio, analytics y data para definir métricas, fuentes de datos y asegurar su calidad y disponibilidad.

• Own the end-to-end data quality backlog: intake, analysis, prioritization, definition, tracking, and closure.
• Define severity/impact criteria (patient, compliance, operational, reporting) and SLAs with key stakeholders.
• Perform root cause analysis for issues such as duplicates, missing/invalid values, inconsistent definitions, referential integrity problems, mapping errors, and out-of-standard data.
• Implement remediation actions: validation rules, deduplication, normalization, reconciliation, and preventive monitoring.
• Design and maintain data quality controls (tests, rules, scorecards) and alerting to prevent recurrence.
• Work confidently with standard/custom objects, relationships, security/access model considerations, integrations, and processes impacting data quality.
• Ensure solutions follow Salesforce data management standards and best practices (governance, naming conventions, ownership, stewardship, lineage/traceability where applicable).
• Collaborate with PO/BA/Engineers/QA to turn data issues into actionable Salesforce-related data quality user stories.
• Lead/participate in refinement: scope definition, acceptance criteria, test approach, release plan, and validation.
• Support execution and verify outcomes in lower environments and production.
• Provide guidance and recommendations on data strategy, data management standards, and sustainable quality practices.
• Propose a 2026 roadmap of quick wins and structural improvements to improve reliability and reduce recurring issues.

Data Engineer – Salesforce/SAP

• Lead end-to-end data migrations from legacy systems, such as SQL Server, custom databases, or other Salesforce orgs, into Salesforce (NPSP or NPC).
• Participate in discovery and design sessions to understand legacy data structures and define the migration strategy, covering data cleansing and transformation, field mapping, alignment with the Salesforce data model, testing and validation, and data access considerations.
• Design and document data mapping logic, ensuring accurate field alignment between source and target systems.
• Design and implement data transformations and cleansing scripts in Python and SQL.
• Prepare datasets for import and perform pre- and post-load validation to ensure data integrity.
• Lead client-facing validation sessions to confirm design assumptions, review results, and resolve data-related issues collaboratively.
• Maintain all scripts and documentation in GitHub using version control.
• Collaborate with project managers, solution architects, and business analysts to align migration work with system design.
• Manage multiple concurrent migration efforts, balancing priorities and communicating progress and risks to stakeholders.
• Develop and maintain reusable migration frameworks and templates to ensure consistency and repeatability across projects.

Data Migration Engineer

• At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth.
• Your work will help transform industries.

• Build and own end-to-end data pipelines in Snowflake — from raw ingestion through transformation to serving layers for AI products
• Partner with ML engineers and data scientists to build and maintain AI-specific data infrastructure
• Consolidate fragmented data sources across the organization into reliable, automated pipelines
• Design scalable data models and marts that serve both analytics and ML feature engineering
• Support rapid iteration on new data products and features in a fast-moving environment
• Collaborate cross-functionally with analytics, data science, and product teams to translate requirements into data solutions
• Proactively monitor and resolve data quality issues, optimizing for cost and performance
• Employ AI technologies to enhance and optimize business processes
• Utilize and leverage Power Digital's Nova ecosystem as it relates to your department
• Use AI coding tools as part of your daily development workflow to accelerate pipeline development and data quality work

See all jobs on JobTailor

Ingeniero de Datos, Databricks

Data Engineer

Junior Data Engineer

Data Engineer – AWS

Lead Data Engineer – Databricks

Data Engineer – Mid-Level

Senior Data Engineer

Data Engineer, Azure, SQL

AWS Data Engineer

Data Engineer – Pipelines, APIs, AI Data Systems

Data Engineer – Data Pipelines, Modeling

Data Engineer

Data Engineer

Senior Data Architect, English Required

Data Engineer

Data Engineer – Salesforce/SAP

Data Migration Engineer

Data Engineer

AI Data Engineer