• Designing, developing, and implementing efficient and scalable data pipelines • Building and maintaining modern data infrastructure • Optimizing the performance of data platforms • Implementing security and compliance best practices • Working closely with data science and analytics teams • Researching and evaluating new data engineering tools and technologies • Creating and maintaining technical documentation

• Diseñar arquitecturas analíticas modernas • Optimizar rendimiento y calidad de datos • Implementar soluciones utilizando Azure Synapse Analytics y Microsoft Fabric

• Work with multiple scrum teams (each has 7-9 engineers), and act as a force multiplier by coaching, mentoring, and developing high-performing data engineering teams and individuals. • Establish and uphold high standards for code quality, readability, and maintainability across multiple engineering teams. • Quickly and confidently navigate large, unfamiliar codebases, making sound technical decisions in ambiguous or evolving environments. • Own and drive the data engineering approach to data quality, including framework design, enforcement, and ongoing improvement. • Lead engineering teams through complex production incidents and outages, driving effective triage, root cause analysis, and durable remediation. • Guide teams toward mature, high-performing DataOps practices that improve reliability, observability, and delivery velocity. • Apply deep expertise in SQL best practices, with an emphasis on performance optimization, readability, and long-term maintainability. • Demonstrate strong understanding of conceptual, logical, and physical data modeling, and apply these principles effectively at enterprise scale. • Solve complex, enterprise-scale data engineering challenges across GTM systems, balancing technical rigor with business impact. • Define, standardize, and enforce testing frameworks and quality gates for data engineering workloads. • Serve as a technical decision-maker for best practices, resolving tradeoffs and driving alignment across teams when standards or approaches are unclear. • Business domain experience in subscription and consumption business models. • Work closely with different stakeholders: Business owners, users, product managers, program managers, architects, engineering managers & developers, etc. to translate business needs and product requirements to well-documented engineering solutions. • Constantly communicating updates to stakeholders and other partners with stakeholders in different phases in terms of requirements clarification, solution/planning review, status/progress sharing etc.

• Translate business requirements into technical specifications, data models, data streams, and databases • Convert or embed ML/AI workflows into production-grade, enterprise systems • Design, develop, and maintain infrastructure for geospatial analysis and ML/AI applications on large data • Develop API-driven backend services with FastAPI, Pydantic, and async Python • Work with columnar analytics stacks (DuckDB, PyArrow, Parquet / GeoParquet) • Deploy monitoring tools to track status and performance of system architecture and data flows • Propose enterprise data architecture solutions in support of business development.