Role Description UPLabs is a dynamic venture studio dedicated to building innovative startup companies from the ground up. Our team thrives on solving complex problems, driving technological advancements, and creating impactful digital products. We’re seeking a highly skilled professional to join our growing team and contribute to our mission of launching the next wave of AI powered solutions for enterprise customers. Technical Challenge: - Design, build, and evolve scalable data pipelines and data platforms to reliably power AI/ML models and agentic workflows for our customers. - Partner closely with forward deployed product and engineering teams to translate ambiguous requirements into robust, maintainable data solutions. Responsibilities: - Architect, build, and maintain scalable, production-grade data pipelines and data models to enable reliable ingestion, transformation, and delivery of data. - Own end-to-end pipeline reliability, including orchestration, monitoring, alerting, and incident response to meet data freshness and quality expectations. - Partner with product, engineering, and analytics stakeholders to define data requirements, translate them into clear technical specifications, and deliver iteratively. - Improve performance, scalability, and cost efficiency of data workloads by tuning SQL queries, optimizing storage/compute patterns, and standardizing best practices across projects. - Document data models, pipeline behavior, and operational runbooks to ensure maintainability and smooth knowledge transfer across accounts. Qualifications - Strong data engineering expertise, including designing and operating data pipelines, data models, and batch/stream processing workflows in a production environment. - Proficiency with Python for building data pipelines, automation, and data tooling. - Advanced SQL skills for data transformation, analysis, and performance tuning. - Experience working with PostgreSQL, including schema design, query optimization, and data integrity best practices. - Experience building data pipelines and workloads on Databricks. - Experience using dbt to develop, test, and maintain modular analytics engineering workflows. - Experience working with MongoDB or other document databases as part of modern data stacks. - Experience working with one or more major cloud platforms: AWS, Azure, or GCP. Preferred Skills - Experience using Snowflake for cloud data warehousing, modeling, and analytics workloads. - Working knowledge of Apache Spark for large-scale data processing. Company Description We build high-growth technology startups that enable faster, cleaner, and safer movement of people and goods. Our vision is to transform the moving world by pairing leading corporations and entrepreneurs with a proven methodology for launching and scaling software and hardware companies. We work with corporate investors over a multi-year period to launch a portfolio of mobility-focused ventures. Our team is dedicated to the first year of a new venture’s life cycle, from ideation to minimum viable product build (and beyond) to recruiting and hiring the full-time team who will scale the business. Location LATAM Remote

• Atuar no desenvolvimento e implementação de soluções de gerenciamento, processamento e análise de grandes massas de dados. • Essas soluções permitirão tanto a alimentação de modelos de Ciência de Dados e de Inteligência Artificial quanto a análise direta dos dados pelos usuários de negócio. • Para tal, será necessário interagir fortemente dentro dos times de Ciência de Dados e Analytics e com os usuários de negócio, apoiando-os nas análises. • As soluções envolvem o tratamento de grandes massas de dados, a integração de fontes variadas de dados e/ou tratamento de dados com grande velocidade, visando a criação de vantagens competitivas para a Companhia apoiadas no uso intensivo de dados.

Role Description The Sr. Fabric Technical Data Architect reports to the CIO, in the IT Department. This role is responsible for designing, building, and operationalizing the company’s Microsoft Fabric-based data platform and lakehouse architecture. This role owns the technical execution layer of the enterprise data program, translating business data models into scalable, governed, and high-performing solutions. It works in close partnership with the Sr. Data & Solutions Architect, who owns the business information model, data strategy, and the definitions of what the data means. This role owns how that model is built and run: the platform architecture, pipelines, governance, and operations that bring it to life. Together, this partnership is critical to evolving from an MVP data platform to a sustainable, in-house enterprise data capability supporting reporting, analytics, and future AI initiatives. Responsibilities - Fabric Platform Architecture - Design and manage end-to-end Microsoft Fabric architecture, including: Lakehouse structure (Bronze / Silver / Gold); Workspace and environment strategy (Dev / UAT / Prod); and Data integration and orchestration patterns. - Define and enforce technical standards for scalability, performance, and cost efficiency. - Establish architectural roadmaps aligned to analytics and business objectives. - Data Engineering & Integration - Build and maintain data ingestion pipelines across systems (D365 F&O, CRM, Fabrication, Service, etc.). - Implement transformation logic to produce curated, reporting and analytics-ready datasets. - Establish consistent patterns for moving data from source → lakehouse → semantic layer in batch, streaming, and real-time patterns. - Ensure alignment between technical models and the defined business information model. - Enable reliable, performant datasets for Power BI and downstream analytics. - Governance, Security & Operations - Establish and enforce data governance frameworks, including: data quality standards; metadata and cataloging; and lineage and observability. - Define and manage security and access controls across data platform components. - Support evolution toward a formal data governance and ownership model. - DevOps & Delivery - Own deployment pipelines, CI/CD processes, and release management for Fabric. - Enable structured incremental delivery supporting testing, UAT, and production stabilization. - Platform Expansion & Future Readiness - Expand the platform beyond core ERP data into additional enterprise systems. - Ensure data architecture is AI-ready (clean, governed, accessible). - Continuously optimize for performance, cost, and usability. - Evaluate and recommend new technologies and tools aligned with strategic direction. Qualifications - Background in industries with complex operational data (construction, fabrication, or project-based environments). - Experience with: - Supporting analytics, reporting, and AI use cases. - Semantic modeling and analytics platforms (e.g., Power BI). - Data cataloging, lineage, and metadata management tools. - Exposure to AI/ML data platform design or analytics enablement. - Experience leading or mentoring technical teams. - Ability to operate effectively in MVP → scale/maturity transitions. Requirements - Bachelor's degree (B. S.) in Software Engineering, Computer Science, Information Systems, Data Science, Mathematics, from a four-year college or university or equivalent combination of technical training and/or related field experience. Benefits - Competitive pay with bonus opportunities. - Medical, prescription, dental & vision coverage with zero to low weekly premiums. - 401(k) with discretionary company match. - Generous paid time off (vacation, sick time, and paid holidays). - Disability income protection. - Life insurance for employees and eligible dependents. - Optional added protection with critical illness and accident plans. - Career growth & development opportunities. - In-house training programs. - Certificate & tuition reimbursement. - Wellness program. - Employee Assistance Program (EAP).

• Design, build, and own the data pipelines that move and transform data from our application and third-party sources into relational databases - keeping them reliable and well-modeled as volume and complexity grow • Partner with analytics engineering to build the data models and reporting that give researchers and teams real visibility into how their work is performing • Own data quality as a first-class concern across ingestion, modeling, and reporting. Catch problems before they reach a model, a dashboard, or a user, and fix them • Build and ship production AI systems the data infrastructure and services that ML features run on • Design and own evaluation systems that tell us whether an AI feature is ready to ship and holding up over time: eval harnesses, test datasets, and production monitoring built as software, not one-off analyses • Set the standard for how data work gets done. Write clearly, share context early, and make the people around you faster