• Build data ingestion pipelines to extract and transform enterprise data. • Implement data cleansing and normalization routines. • Write and maintain ETL jobs using Spark/PySpark on cloud infrastructure. • Implement data validation and quality checks at each pipeline stage. • Build automated data export jobs for model training datasets. • Support feature extraction from enterprise schemas. • Monitor pipeline health, troubleshoot failures, and optimize performance. • Document data lineage, schemas, and transformation logic.

• Diseñar, desarrollar, implementar y ajustar sistemas distribuidos a gran escala y canalizaciones que procesan grandes volúmenes de datos • Centrándose en la escalabilidad, la baja latencia y la tolerancia a fallos en cada sistema construido

• Define and maintain the end-to-end application and data architecture • Establish standards for system design, integration patterns (APIs, middleware, eventing), and data models • Ensure scalability, performance, and long-term maintainability • Act as the technical lead across all external development partners • Review and approve solution designs, code architecture, and technical approaches • Challenge vendors where needed — no rubber stamping • Ensure delivery aligns with architectural standards and business outcomes • Define and oversee data architecture, governance, and quality standards • Manage integration across systems (ERP, CRM, eCommerce, etc.) • Ensure data is usable, reliable, and decision-ready • Partner with leadership to translate business goals into technical roadmaps and system requirements • Simplify complex technical concepts for non-technical stakeholders • Evaluate and guide decisions on SaaS vs. custom development, build vs. buy vs. integrate • Design integration architecture across ERP, marketing systems, and data platforms • Establish architecture governance processes • Participate in sprint reviews, backlog prioritization, and delivery checkpoints • Ensure proper documentation, testing, and deployment standards

• Utilize consulting and technical skills to be able to work in a client-facing project environment independently • Be responsible for your own execution and sometimes lead individual work streams on client engagements as assigned and under supervision of engagement lead • Collaborate with other team members to successfully deliver on projects • Work effectively and directly communicate with both internal and client and/or partner teams • Develop full ownership of your execution on client engagements • Design and implement complex ETL/ELT pipelines with evidence of improved data processing times • Successfully lead small data warehousing projects with measurable performance enhancements under management of an engagement lead • Contribute to real-time data processing solutions and manage streaming data • Implement security and compliance measures for data pipelines • Design and implement version control and branching strategies and integrate them into CI/CD for promoting and testing in higher environments • Hands-on experience working with SAP data at the table level • Strong understanding of SAP data structures and relationships, beyond ETL tooling • Ability to interpret SAP data in the context of underlying business processes