• Design, implement, and optimize data engineering architectures and processes, ensuring scalability, security, and efficiency. • Develop and implement data engineering solutions in Azure environments (Data Factory, Synapse Analytics, Databricks, Data Lake, SQL Database). • Build high-performance, reliable data pipelines (ETL/ELT). • Work on data modeling, system integration, and information governance. • Ensure best practices for performance, security, and scalability. • Collaborate with multidisciplinary teams and international stakeholders. • Participate in meetings and presentations in advanced English.

• Build, test, and document Snowflake data models and business logic in dbt • Apply and improve data quality, testing, observability, and lineage standards • Collaborate with cross-functional partners to define data contracts and interfaces • Contribute to Capital Rx’s modular data platform and client-specific data configurations • Participate in design reviews; propose scalable, maintainable patterns • Monitor pipeline health, troubleshoot incidents, and drive root-cause fixes • Optimize cost and performance of jobs, storage, and queries with guidance • Write clear documentation and support knowledge sharing • Adhere to the Capital Rx Code of Conduct, including reporting of noncompliance

• Partner closely with operational and analytics teams to translate business needs into analytics-ready data models, curated data marts, and reusable datasets. • Build and maintain dbt projects (staging → intermediate → marts), including model layering, macros, exposures, packages, and source freshness to accelerate delivery and improve reliability. • Design and evolve dimensional models (facts/dimensions, grain, SCD patterns) and analytics patterns aligned with key business domains. • Own metrics definitions end-to-end: establish KPI logic, naming standards, calculation rules, and governance; maintain a metrics/semantic layer so stakeholders use consistent definitions across tools and teams. • Implement robust data quality practices (dbt tests, constraints, anomaly checks/monitoring) to ensure reliable downstream reporting and decision-making. • Produce clear documentation and enablement assets (dbt docs, lineage, data dictionaries, metric definitions, onboarding guides) to support self-service analytics. • Optimize warehouse performance and cost in Snowflake through SQL tuning, incremental models, appropriate materializations, and efficient data design. • Use disciplined software engineering practices (Git-based workflows, peer review, CI checks, versioning, environment promotion) to deploy changes safely and repeatably. • Proactively identify and resolve data-related issues, ensuring system reliability and data integrity. • Provide ongoing support for critical analytics data products and participate in an on-call rotation as needed. • Responsible for adherence to the Capital Rx Code of Conduct including reporting of noncompliance.

• Partner closely with operational and analytics teams to translate business needs into effective data solutions • Architect and implement ETL workflows leveraging CapitalRx platforms and technologies such as Python, dbt, SQLAlchemy, Terraform, Airflow, Snowflake, and Redshift • Conduct rigorous testing to ensure the flawless execution of data pipelines before production deployment • Proactively identify and resolve data-related issues, ensuring system reliability and data integrity • Provide ongoing maintenance and support for critical data infrastructure, including 24x7 on-call availability • Responsible for adherence to the Capital Rx Code of Conduct including reporting of noncompliance.