• Act as a trusted technical advisor across client engagements. • Build strong relationships with senior stakeholders, delivery leads and engineering teams. • Translate complex technical concepts into clear business value and delivery outcomes. • Support clients through technical decision-making, platform strategy and roadmap planning. • Lead the design and implementation of enterprise-scale Databricks solutions and modern data platforms. • Provide architectural leadership across cloud-native data engineering initiatives. • Define engineering standards, best practices and scalable delivery approaches. • Drive platform optimisation, resilience and performance improvements. • Lead technical delivery across multiple client engagements within a consultancy environment. • Work closely with cross-functional teams including Data Engineers, Architects, Product Owners and client stakeholders. • Support project planning, technical governance and delivery execution. • Ensure high-quality, scalable and maintainable engineering solutions. • Mentor and guide engineering teams across modern data engineering practices. • Lead technical problem-solving and incident resolution where required. • Drive adoption of engineering best practices, automation and DevOps principles.

• Design, develop, and maintain robust, scalable ETL/ELT data pipelines using Python, SQL, and data processing frameworks including dbt, Matillion, and AWS services. • Implement data quality checks, monitoring, and alerting across all data pipelines to ensure data integrity and reliability. • Optimize existing pipelines for performance, cost-efficiency, and error handling. • Contribute to the design and maintenance of InStride’s data warehouse and data lake solutions, including schema design, data modeling, and indexing strategies in Amazon Redshift. • Ensure data security, HIPAA compliance, and proper handling of protected health information (PHI) within all data infrastructure. • Work closely with data analysts, data scientists, and business intelligence engineers to understand their data requirements and deliver reliable, high-quality data access. • Troubleshoot and resolve complex production data issues with urgency and root-cause rigor. • Develop and maintain clear documentation for data models, pipelines, and data sources to enable self-service analytics. • Participate in code reviews and contribute to technical discussions, bringing a constructive and detail-oriented perspective. • Stay current on emerging data technologies and tools, bringing relevant insights to the team.

Role Description Integrarte a un equipo de datos para diseñar, desarrollar y operar pipelines ETL/ELT en Azure, garantizando la ingesta, transformación y disponibilidad de datos para consumidores de negocio y modelos analíticos. El rol implica responsabilidad técnica en la implementación de soluciones con Azure Data Factory, Azure Databricks y ecosistemas de almacenamiento (Blob Storage, Data Lake) aplicando buenas prácticas de calidad, rendimiento y gobernanza de datos. Responsibilities - Diseñar y desarrollar pipelines de ingestión y transformación de datos utilizando Azure Data Factory y Azure Databricks. - Implementar procesos ETL/ELT con Apache Spark y PySpark para procesamiento por lotes y en tiempo casi real. - Modelar y optimizar consultas SQL y estructuras de datos para consumo analítico y reporting. - Gestionar y organizar datos en Azure Blob Storage y Azure Data Lake (Gen2), definiendo particionamiento, formatos y políticas de retención. - Construir pipelines reproducibles, parametrizables y observables; instrumentar logging, métricas y alertas. - Colaborar con equipos de ciencia de datos, BI y producto para definir contratos de datos, SLAs y requisitos de calidad. - Automatizar despliegues y pruebas de pipelines; aplicar control de versiones y prácticas CI/CD para artefactos de datos. - Garantizar cumplimiento de políticas de seguridad y privacidad en el manejo de datos sensibles. - Documentar arquitecturas, pipelines y runbooks; transferir conocimiento al equipo y participar en on-call rotativo cuando aplique. Qualifications - Experiencia comprobable como Data Engineer (mín. 3 años) trabajando con Azure Data Factory y Azure Databricks. - Dominio de Apache Spark y PySpark para procesamiento distribuido y optimización de jobs. - Sólidos conocimientos de SQL: modelado, tuning y optimización de consultas. - Experiencia en diseño e implementación de pipelines ETL/ELT y en integración de datos. - Conocimiento práctico de Azure Blob Storage y Azure Data Lake (Gen2), incluyendo formatos como Parquet/Delta Lake. - Familiaridad con herramientas de orquestación, monitorización y CI/CD aplicadas a datos. - Inglés técnico: nivel intermedio (lectura de documentación y comunicación con equipos técnicos). - Residir en Argentina. Bonus Points - Experiencia con Delta Lake, optimizaciones de storage y compaction. - Conocimientos en seguridad y gobernanza de datos: RBAC, Data Catalog, políticas de enmascaramiento. - Familiaridad con Python avanzado y librerías de ingeniería de datos (pandas, numpy, airflow o equivalentes). - Experiencia con performance tuning de Spark y troubleshooting de jobs en Databricks. - Certificaciones Azure relacionadas con datos (DP-203, Azure Data Engineer Associate) o Databricks. - Experiencia en entornos cloud a gran escala y en trabajo ágil con equipos multidisciplinarios. Selection Process - Recruiting screen - Entrevista técnica - Entrevista con el cliente - Oferta Benefits - Envía tu solicitud y únete a nosotros para impulsar soluciones de datos robustas y escalables. - Para postular: - Envía tu CV a través de nuestro portal. - Completa tu perfil en talentconnect.ai. - Participa en una entrevista inicial utilizando inteligencia artificial. - Al completar estos pasos estarás más cerca de sumarte a nuestro equipo.

Role Description We are looking for a Data Engineer Analyst to contribute to the design and implementation of scalable, domain-oriented data solutions for a high-impact enterprise initiative. You will work within a specific data domain — such as Ingestion, Customer Data, Enrichment, or Marketing Signals — ensuring high-quality, reliable data pipelines that power analytics and business-critical workflows. You may work 100% remotely from anywhere in Mexico! - Contribute to the design and development of scalable data pipelines on AWS (Glue, S3, Athena, Step Functions). - 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. Qualifications - Bachelor's degree in Computer Science, Data Engineering, Software Engineering, or equivalent field. - Hands-on experience with the AWS ecosystem, including Glue, S3, Athena, and Step Functions (required). - Solid proficiency in Python and PySpark for data pipeline development (required). - Experience with Snowflake or similar cloud data warehouse platforms (plus). - Understanding of data pipeline architecture and design patterns. - Exposure to CDC (Change Data Capture) patterns and streaming ingestion. - Experience with data modeling for analytics and reporting use cases. - Familiarity with AI-assisted development tools (plus). - Strong problem-solving skills and ability to work effectively in collaborative, agile environments. - English: Advanced (required for effective communication with global teams). Requirements - 3+ years of experience in Data Engineering or related disciplines, with hands-on work building and maintaining data pipelines on cloud platforms such as AWS. Benefits - Learning Opportunities: - Certifications in AWS (we are AWS Partners), Databricks, and Snowflake. - Access to AI learning paths to stay up to date with the latest technologies. - Study plans, courses, and additional certifications tailored to your role. - Access to Udemy Business, offering thousands of courses to boost your technical and soft skills. - English lessons to support your professional communication. - Travel opportunities to attend industry conferences and meet clients. - Mentoring and Development: - Career development plans and mentorship programs to help shape your path. - Celebrations & Support: - Special day rewards to celebrate birthdays, work anniversaries, and other personal milestones. - Company-provided equipment. - Flexible working options to help you strike the right balance. - Other benefits may vary according to your location in LATAM. For detailed information regarding the benefits applicable to your specific location, please consult with one of our recruiters.