• Build and ship internal applications, services, and shared libraries end to end from API to the data layer. • Design, query, and tune our managed data platforms while keeping schemas, migrations, and query performance healthy. • Maintain the internal SDK that routes apps through the model gateway, injects keys, and adds tracing and cost headers, so teams write less code and we keep control of prompts and guardrails. • Run and harden the model gateway used by every team, including high availability across zones and clean dev, staging, and production environments. • Build and operate the cloud services behind the platform, including container compute, managed databases, caching, storage, and secrets. • Build and run the CI/CD pipelines, container registry, and monorepo tooling that every app depends on. • Own platform security maintenance by patching CVEs and security findings quickly during business hours. • Work with the Security team to implement core safety controls, including image and code scanning, secret scanning, access control, identity and sign-on, and audit logging. • Keep the platform reliable and observable through logging, tracing, issue resolution, and steady improvement. • Support cost visibility by tagging usage, building spend and chargeback views, and helping keep spend sensible. • Write clear documentation and provide practical support so teams can adopt the platform without friction.

• Design, build, and maintain scalable data pipelines for analytics, ecommerce, logistics, and marketing. • Improve and maintain the company's data infrastructure and tooling, including Redshift/Snowflake, Airflow, and Fivetran. • Architect data processing systems capable of handling complex data flows and large-scale datasets. • Develop reliable, efficient, and cost-effective data solutions in an AWS environment. • Ensure high standards of data quality through automation and best engineering practices. • Optimize platform performance, resiliency, scalability, and operational costs. • Collaborate closely with software engineers, analysts, and business stakeholders. • Mentor engineers and analysts on data engineering best practices. • Participate in architecture discussions and technical decision-making.

• Architect and develop .NET systems with a focus on performance and scalability; • Collaborate with multidisciplinary teams on the design and implementation of solutions; • Maintain and evolve existing systems following engineering best practices; • Integrate systems with AWS services; • Contribute to the continuous improvement of team processes and practices.

• Design, develop, and maintain data ingestion and transformation pipelines in an Azure Data Lakehouse. • Build solutions using Azure Databricks with PySpark and SQL for data processing and modeling. • Implement and evolve integration and orchestration pipelines using Azure Data Factory. • Perform data audits, validations, and reconciliations using advanced SQL queries. • Monitor pipeline executions, logs, and performance, proposing improvements for performance, architecture, and cost. • Document pipelines, data flows, and architecture following corporate standards. • Work on integration across multiple data sources and environments. • Ensure best practices in data governance, quality, and security.