¡Trabaja en DaCodes! Somos una firma de expertos en software y transformación digital de alto impacto. Durante 10 años hemos creado soluciones enfocadas en la tecnología e innovación gracias a nuestro equipo de casi 300 talentosos #DaCoders, incluyendo desarrolladores, arquitectos, diseñadores UX/UI, PMs, QA testers y más. Nuestro equipo colabora en proyectos con clientes en LATAM y Estados Unidos, logrando resultados sobresalientes. En DaCodes, tendrás la oportunidad de impulsar tu desarrollo profesional, trabajar en diversos proyectos dentro de distintas industrias, y contribuir al diseño, implementación y optimización de infraestructuras en la nube. Nuestros DaCoders tienen un gran impacto en el éxito de nuestro negocio y el de nuestros clientes. Serás el experto que participará en nuestros proyectos y tendrás acceso a startups disruptivas y marcas globales. ¿Te interesa? Buscamos un Data Solutions Engineer con perfil técnico híbrido, capaz de desenvolverse tanto en el desarrollo de software como en la construcción de soluciones de datos modernas. Este rol combina mantenimiento de sistemas existentes, desarrollo de nuevos componentes y la implementación de pipelines de datos utilizando tecnologías como Microsoft Fabric y Databricks. Es ideal para alguien con mentalidad práctica, orientado a la ejecución, que pueda moverse con soltura entre entornos legacy y procesos de modernización tecnológica. Tendrás un rol activo en la construcción de soluciones end-to-end, colaborando con equipos técnicos y de negocio para resolver problemas reales mediante tecnología.

• Upload and organize patient records from multiple data sources (CSV, spreadsheets, clinical documents) • Prepare and structure large datasets for system migration • Ensure accurate and efficient data transfer into the platform • Clean and standardize datasets from external systems • Identify missing, duplicate, or inconsistent data • Normalize formats (names, dates, medical data) • Validate data accuracy before and after migration • Utilize AI tools to extract and process patient data • Generate structured patient records • Review and validate AI-generated outputs for accuracy • Perform detailed QA checks to ensure near-zero error rates • Identify and resolve issues proactively before they impact implementation • Maintain high standards of data integrity and consistency • Update implementation trackers and progress reports • Communicate updates, blockers, and issues clearly with the team.

• Assist in developing and maintaining data pipelines and ETL/ELT processes under the guidance of more senior engineers. • Write Python and SQL to extract, transform, validate, and load data from common sources. • Perform data quality checks (validation, reconciliation, basic monitoring) and help troubleshoot data issues. • Develop dashboards and analytic products using data visualization tools (e.g., Power BI, Tableau). • Support cloud-based data workloads (e.g., Azure/AWS/GCP basics) and learn platform-native services and patterns. • Document pipeline steps and technical processes to support maintainability and knowledge transfer. • Participate in team delivery rhythms (standups, sprint ceremonies) and contribute to reviews with a learning mindset. • Design, build, test, and maintain scalable data pipelines (batch and/or streaming as applicable) with increasing independence. • Integrate data from multiple sources, resolve inconsistencies, and deliver curated datasets for analytics and operational use. • Own data quality for assigned domains by implementing validation checks, reconciliation, and monitoring/alerting patterns. • Build, maintain, and deploy data products for analytics and data science teams on cloud platforms (e.g. AWS, Azure, GCP). • Optimize performance of pipelines and queries (tuning, partitioning patterns, efficient compute usage). • Collaborate cross-functionally with analysts, data scientists, and stakeholders to translate requirements into technical designs and delivery plans. • Produce and maintain technical documentation for data flows, data models, and operational procedures. • Contribute to governance and compliance practices (access controls, lineage awareness, controlled data handling) within your scope. • Lead the design and build of scalable data pipeline architectures and tools, including patterns for reliability, security, and maintainability. • Drive ETL/ELT and data quality strategy (frameworks, standards, repeatable testing/monitoring approaches) and raise engineering maturity across the team. • Architect solutions in cloud data platforms (e.g., Azure + Databricks, Snowflake) and guide implementation tradeoffs (cost, performance, scalability, governance). • Design data stores and interactions across storage types (relational, warehouse, lake/lakehouse, and NoSQL where needed) aligned to use cases. • Enable data science / ML readiness by delivering well-modeled, reliable, well-documented datasets and features. • Lead requirements gathering and technical planning; translate ambiguous problem statements into actionable architectures, backlogs, and delivery increments. • Champion data quality and governance standards through the development of sophisticated data quality frameworks, dashboards, and feedback loops to ensure transparency in data completeness, consistency, and quality for partners and researchers. • Own client and stakeholder engagement for your workstream, including organizing/leading meetings, producing clear written outputs, and tracking follow-through. • Mentor and review: provide strong code/design reviews, coach engineers, and help remove technical blockers.

• Lead, mentor, and grow a team of data engineers and architects • Define and execute the technical roadmap for production database systems (MySQL, PostgreSQL, DynamoDB, Elastic) • Own the architecture and governance of binlog replication, logical replication, and CDC workflows • Drive strategy and reliability for ELT/ETL pipelines and Kafka-based streaming architectures • Set standards for performance optimization, query tuning, indexing, and database scaling across teams • Oversee backup, failover, disaster recovery (PITR), and incident response for all production data systems • Drive cost efficiency, infrastructure optimization, and monitoring across cloud-managed data services (AWS RDS, Aurora, DynamoDB) • Champion data integrity, security, and compliance standards across all data engineering work • Partner cross-functionally with backend, data science, infrastructure, and product teams to align on data platform priorities • Establish engineering guardrails, best practices, and documentation to enable team autonomy and quality at scale • Lead the evaluation and selection of next-generation data warehousing technology (Snowflake, Databricks, AWS Redshift Serverless) — assessing performance, cost, ecosystem fit, and migration complexity to inform a platform decision • Own the design of an upgraded data model for the warehouse in partnership with data engineers and architects, establishing standards for schema design, partitioning, access patterns, and downstream consumption • Oversee the end-to-end migration from the current Redshift warehouse — planning the phased approach, managing cutover risk, and ensuring continuity of downstream reporting and analytics throughout