Top Tech Jobs & Startup Jobs in San Antonio, TX

Lead and grow Revance's digital engineering capability across Salesforce and modern headless web stacks. Hands-on manager who debugs production issues, enforces code quality and standards, manages vendor partnerships, hires and develops engineers, drives technical debt remediation, and owns platform reliability and cross-pod engineering consistency.

Design and prototype intuitive Power Apps interfaces and modern SharePoint experiences. Develop wireframes, forms, information architecture, reusable UI components, and branding. Conduct stakeholder workshops, map user journeys, ensure accessibility and usability, and collaborate with developers through design specs, reviews, and UAT to deliver Power Platform and SharePoint solutions.

Lead and manage a team developing the Aerospike Cloud Platform, focusing on architecture, execution, and fostering a high-performance engineering culture.

The AI/ML Engineering Manager leads a team of ML engineers and architects, managing hiring, performance, and customer engagements while providing technical guidance in ML operations and architecture.

As a Senior Backend Engineer, you will design and deliver complex systems, ensuring reliability, security, and performance while mentoring junior engineers and collaborating across teams.

The Salesforce Developer will lead Salesforce development and administration, including customizations, integrations, and process automation, while ensuring data integrity and user support.

The Senior Product Manager will lead product strategy, work cross-functionally to develop and implement digital products, and ensure a user-centered experience for opioid use disorder treatment.

The Senior Backend Data Engineer will extend data ingestion and processing pipelines, build APIs and backend services, and provide technical leadership.

The Enterprise Architect will define and implement a cloud-ready architecture integrating 22+ products, ensuring data interoperability, and leading modernization efforts across teams.

Lead and scale a production-focused data science practice: define engineering standards, build reusable Python frameworks, drive end-to-end model development and monitoring, collaborate with Data Engineering/MLOps/analytics and business partners, mentor team members, and establish measurement, documentation, and prioritization disciplines to ensure models deliver business impact across Claims, Underwriting, Risk, and Distribution.