• Develop end-to-end ELT pipelines that integrate data from multiple sources to power a reliable and scalable data platform • Design and build high-performance, reusable data models that efficiently handle large datasets and support analytics, marketing, and machine learning use cases • Create and maintain APIs that enable seamless data sharing across systems, including APIs for deploying and serving machine learning models in production • Partner with engineering, data, and business teams to drive and deliver impactful data engineering initiatives • Contribute meaningfully to the design and implementation of core data infrastructure decisions

• As a Principal Data Architecture Consultant, you act as a senior technical leader in complex data and analytics engagements. • You shape and govern end-to-end enterprise data architectures, lead technical teams, and serve as a trusted technical advisor for clients and internal stakeholders. • You are responsible for ensuring that enterprise data and analytics solutions are scalable, secure, and production-ready, while translating business requirements into robust technical designs and delivery roadmaps. • Define and govern target enterprise data, integration and analytics architectures across cloud and hybrid environments • Translate business objectives into scalable, secure, and compliant data solutions • Lead the design of end-to-end data solutions (ingestion, integration, storage, security, processing, analytics, AI enablement) • Guide delivery teams through implementation, rollout, and production readiness • Function as senior technical counterpart for client architects, IT leads, and engineering teams • Mentor data architects, system architects and engineers and contribute to best practices and reference architectures • Support pre-sales and solution design activities from a technical perspective

• Implementation of the Data Science models delivery pipeline to Production in collaboration with Data Science • Integration and collection of data from different internal and external sources into a single Data Warehouse (ETL procedures) • Improving infrastructure for experimentation, storage of results, retraining of models, quality monitoring, and alerting • Automation of data collection and analysis processes • Making architectural decisions when developing a data storage and processing system • Monitoring of data sources • Interaction with analysts and programmers in the process of designing and implementing tasks

• Build and maintain robust, scalable data pipelines using Airflow, Python, and SQL. • Design, build, optimize data pipelines using dbt, or sqlmesh. • Develop and manage the ODS and support the eventual rollout of a modern Data Warehouse. • Integrate data from internal systems and external APIs to create clean, reliable datasets. • Work closely with engineers to operationalize machine learning workflows. • Ensure high data quality through monitoring, validation, and error handling. • Provide guidance to less experienced team members and champion data engineering best practices. • Deploy and manage infrastructure in the cloud (AWS, GCP, or Azure) using modern DevOps tooling. • Implement monitoring and alerting to ensure data pipelines are reliable and maintainable.