• Apply data expertise and automation principles to generate accurate, customer-facing scientific content. • Serve as a subject matter expert (SME) for data handling, standardization, and automation. • Clean, validate, wrangle, and standardize product data and metadata. • Develop and refine data pipelines and content generation processes. • Optimize interactions with the custom in-house SQL database + VB.NET platform. • Identify and implement opportunities to eliminate redundant data entry and reduce errors. • Apply automation, scripting, and data technologies to streamline content development. • Lead continuous improvement initiatives for data flow and best practices in scientific content creation. • Support new product initiatives by ensuring data readiness and accurate generation of materials. • Independently drive and manage project scope, roadmaps, and end-to-end deliverables.

Role Description We are looking for a Data Engineer to join a team of analytics and machine learning experts. The hire will be responsible for: - Building tooling to support analytics - Helping to extend our machine learning platform - Expanding and optimizing our data pipeline architecture - Supervising junior engineers - Interfacing with the Development team to create cross-team solutions The ideal candidate is an experienced data engineer and data wrangler who enjoys optimizing data systems and building them from the ground up. They must be self-directed and comfortable supporting the data needs of multiple teams, systems, and products. Experience in analytics and statistics is a major bonus. The right candidate will be excited by the prospect of optimizing or even re-designing our company's data architecture to support our next generation of products and data initiatives, as well as mentoring and guiding junior members of the team. Qualifications - 3+ years of experience in a Data Engineer role - Bachelor's degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field - Advanced working SQL knowledge and experience with relational databases (including Postgres and MySQL) - Experience building data pipelines, architectures, and data sets from raw, loosely structured data - A history of focusing on test-driven design and results for repeatable and maintainable processes and tools - Experience building processes supporting data transformation, data structures, metadata, dependency, and workload management - Working knowledge of message queuing, stream processing, and highly scalable data stores - Strong project management and organizational skills - Experience managing junior engineers and guiding a team through project planning, execution, and quality control stages Requirements - Experience with object-oriented design in Python - Experience with data pipeline and workflow management tools - Experience with AWS cloud services: EC2, RDS, Redshift, Glue, S3 Additional Pluses - Strong analytic skills and understanding of statistical methodologies - Experience building machine learning models - Experience handling data from acquisition to usage in models - Experience building and maintaining RestAPI systems, Flask apps, and state machines - Experience with continuous integration, especially in a data science context - Experience with Ruby on Rails Benefits - Competitive salary and benefits package - Flexible, remote work - Fun, fast-paced work environment - Dynamic start-up culture - Ability to make an immediate impact in a growth stage company - Convenient downtown Chicago office located in the heart of the city - Equal opportunity employer

• Analyze and translate high-level customer requirements into detailed designs to solve complex business problems • Design solutions that align to the long-term plan for a service or product based on deep expertise and customer insights • Partner with various engineering teams to create and manage a long-term data and information architecture and execution roadmap for a product or service • Define data solutions and develop code across multiple products or services, as well as, influence or drive architectural changes • Ensure consistent, usable, forward-looking, maintainable test infrastructure; draw from a large base of design patterns, is an expert in available technologies, and is adept at identifying practices that work well • Identify code across multiple code bases to optimize, refactor, and reuse code to improve performance and maintainability while ensuring maximum efficiency, effectiveness, and return on investment • Lead code reviews across the product or service, understand the root causes of issues, and find ways to resolve them • Proactively identify performance and availability issues, troubleshoot, provide effective options, and resolve issues in production that could span multiple product areas • Develop and maintain thorough architectural documentation for the product or service • Design products or services by using secure programming patterns and by finding, fixing, and enhancing security in existing applications • Ensure security best practices are part of design and implementation of new features and applications • Estimate for projects that span multiple product areas including work, time, resources and skill needs • Proactively identify technologies or solutions that differ from current technology stack or represent innovative uses of existing technologies • Construct and deliver proposed solution strategies for potential new technologies and work with architecture to review and approve proposals • Mentor and coach other engineers by participating in design and code reviews and share best practices; proactively seek mentorship from others • Lead the effort in defining the engineering lifecycle and practices for team and associated teams in partnership with the principal data engineer • Drive collaboration across multiple teams; find ways accomplish more by enabling others • Anticipate business needs and present options to leadership and business stakeholders with product managers • Other duties or responsibilities as assigned according to the team and/or country specific requirements

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