Top Tech Jobs & Startup Jobs in Chicago, IL

Leads enterprise quality engineering and test automation strategy across UI, API, integration, performance, and data layers. Architects Java-based automation frameworks, integrates quality gates into CI/CD pipelines, and drives AI/LLM adoption for test generation, debugging, self-healing, test data creation, and defect analysis. Establishes ETL and data validation practices, governs DevOps quality standards, mentors QA teams, and collaborates with Engineering, DevOps, Data Engineering, and Product stakeholders to improve quality maturity and delivery predictability.

Design and lead scalable data engineering solutions across Databricks and Microsoft Azure. Define enterprise data modeling standards, architect pipelines and integrations, establish engineering best practices, troubleshoot complex platform issues, and guide modernization and cloud migration initiatives. Provide technical leadership and mentorship while ensuring data platforms meet security, quality, governance, reliability, performance, and maintainability standards.

Design, develop, and maintain scalable data pipelines and models in Databricks and Microsoft Azure. Perform data ingestion, transformation, integration, migration, and modernization across enterprise data sources. Monitor and optimize pipeline performance, reliability, and data quality. Translate business requirements into technical solutions, collaborate with architects and engineers, follow security and governance standards, participate in code reviews, and document data platforms and processes.

Design, build, and deploy production-grade AI applications, agents, retrieval systems, integrations, and workflows for enterprise customers. Lead technical discovery, architecture workshops, prototyping, troubleshooting, deployments, and go-live support. Translate customer needs into scalable solutions and reusable engineering patterns, while providing product feedback that informs roadmaps. Partner with Product, Sales Engineering, Customer Success, and Engineering teams, and mentor customer-facing engineers. Travel to customer sites as needed.

Lead enterprise and domain data modeling across conceptual, canonical, logical, physical, and business models. Translate business concepts into scalable data structures, facilitate stakeholder workshops, establish business glossaries, and align models with governance, metadata, MDM, analytics, AI, reporting, and data stewardship initiatives. Support engineering teams implementing cloud and lakehouse solutions while serving as a domain architecture lead for durable, flexible modern data platforms.

Lead complex, high-value sales cycles into enterprise retail and logistics accounts. Build and manage pipeline through prospecting and executive networking, close multi-stakeholder deals, meet senior-level quotas, and independently own a territory or vertical.