• Analizar los requerimientos técnicos basados en los diseños de ingeniería para asegurar soluciones alineadas a la arquitectura de datos. • Desarrollar productos de datos como ETLs, ELTs y APIs bajo estándares de Clean Code para garantizar eficiencia y fácil mantenimiento. • Diseñar componentes genéricos y lógicas reutilizables en coordinación con el equipo Senior para acelerar los ciclos de desarrollo. • Aplicar estrategias de validación y testing orientado a datos que aseguren el cumplimiento de criterios funcionales y no funcionales. • Resolver incidencias en ambientes de estabilización proponiendo soluciones de largo plazo que mitiguen la recurrencia de errores. • Participar en sesiones de programación en pares para identificar mejoras y elevar el estándar técnico del equipo de desarrollo. • Documentar los productos desarrollados siguiendo los estándares y plataformas oficiales del Banco. • Apoyar en el proceso de onboarding y mentoría técnica para los nuevos integrantes que se incorporen a la unidad de datos.

• Design and build ETL/ELT pipelines and dimensional data models using dbt, Airflow, Python, PySpark, and AWS services (S3, Glue, Lambda) • Create executive dashboards and perform complex SQL analysis to drive strategic decisions (Tableau, Sigma, SAP BO) • Optimize SQL queries, data structures, and warehouse resources for performance and cost efficiency at scale (Snowflake, Redshift) • Partner with stakeholders to translate business requirements into self-service analytics capabilities • Implement infrastructure-as-code (CloudFormation/CDK) and contribute to CI/CD automation • Troubleshoot production issues across data pipelines, queries, and APIs; perform root cause analysis • Provide technical mentorship, establish development standards, and drive data engineering best practices • Document solutions and communicate designs to cross-functional teams in Confluence/JIRA • Apply data governance, security, and monitoring/alerting best practices • Leverage AI-assisted development tools (GitHub Copilot, Claude, etc.) to increase productivity and accelerate delivery

Who We Are Renew Home is on a mission to change how we power the world by making it easier for customers to save energy and money at home as part of the largest residential virtual power plant in North America. We partner with industry-leading brands to better manage residential energy for users by prioritizing efficiency, savings, and comfort — and cleaner energy for everyone. We are an Equal Opportunity employer striving to create a diverse, equitable, and inclusive work environment where everyone feels that they have a voice that is heard. We strongly encourage candidates to check out our website at www.renewhome.com  to learn more about the world-changing work we are doing. What You Will Do - Architect and deploy secure, scalable, and highly available batch and real-time data pipelines.  Implement and optimize data lake architectures for structured and unstructured data from millions of connected devices. - Work closely with development teams to integrate data engineering services into the broader system architecture.  Collaborate with cross-functional teams consisting of engineers, data scientists, and analysts to deliver clean, reliable data. - Analyze and enhance the performance of PostgreSQL Aurora and Redshift databases through query tuning, indexing & partitioning strategies, and efficient resource allocation. - Maintain system performance, data integrity, and uptime. Manage and participate in on-call rotations and ensure strong operational standards. - Contribute to the design and evolution of our data architecture to support growing business needs. - Work with tools and platforms such as Python, Redshift, Postgres, AWS/GCP, AWS Lambda, Kinesis, Prefect (or Airflow), Redis, Git, and Terraform. - Participate in our agile development process, including regular team updates, stand-up meetings, and one-on-ones.

• Develop and maintain scalable data pipelines (batch and streaming) • Ensure efficient ingestion, transformation, and provisioning of data • Model data to support analytics and AI applications • Integrate multiple data sources (APIs, event streams, and internal systems) • Build and optimize jobs using Spark / PySpark • Orchestrate pipelines using Step Functions • Work with event-driven architecture • Organize and structure data in Data Lake / Lakehouse environments • Implement data partitioning and optimization strategies • Ensure pipeline quality, reliability, and observability • Optimize cost and performance in AWS environments • Collaborate with data teams (analysts and data scientists) to deliver solutions • Support technical decisions related to data architecture and engineering