Top Tech Jobs & Startup Jobs

Architects and optimizes high-volume database platforms supporting trading applications. Responsibilities include MySQL schema design, SQL and query optimization, migrations, scalability, reliability, observability, production issue resolution, developer tooling, and database architecture. The role partners with engineering and platform teams, leads technical initiatives, establishes database best practices, mentors engineers, and uses Google Cloud services to improve performance, resilience, and operational efficiency.

Protects delivery commitments for 3–4 globally distributed Scrum teams by managing roadmaps, PI planning, dependencies, risks, milestones, and stakeholder communication. Facilitates Agile ceremonies, coaches teams, removes impediments, supports technical work decomposition, monitors delivery metrics, and drives process improvements. Partners across product, design, engineering, QA, and operations to coordinate releases and ensure software is delivered predictably.

Leads OppFi’s lifecycle and brand marketing strategy across acquisition, retention, repayment, servicing, and collections. Owns omnichannel communications through Braze, lifecycle KPIs, brand standards, creative direction, customer insights, and go-to-market strategy. Manages and mentors lifecycle marketing, design, and marketing technology teams while partnering cross-functionally with Product, Data, Engineering, Operations, Legal, Finance, and executives. Drives experimentation, optimization, customer experience, and responsible marketing within a regulated FinTech environment.

People Operations Generalist managing recruiting coordination, onboarding, HR systems, employee support, benefits administration, payroll support, facilities, and company events. The role maintains employee data, improves workflows through automation and AI, supports compliance, coordinates hiring campaigns, and serves as a trusted resource for employees and managers in a hybrid Denver office environment.

Leads business and system requirements gathering, stakeholder solutioning, documentation, configuration, troubleshooting, quality assurance, testing, and release-note coordination. The role supports customization requests, reviews configurations, oversees project testing, monitors junior business analysts, and helps deliver solutions for fintech and financial-services clients.

Leads the development and ownership of Cedar’s production machine learning personalization engine. Responsibilities include ML modeling, MLOps, feature pipelines, deployment, inference, monitoring, feedback-loop optimization, compliance and fairness guardrails, technical leadership, code reviews, cross-functional collaboration, and management of ML engineers. The role requires hands-on contributions to scalable ML systems serving millions of healthcare transactions while guiding roadmap execution and model improvement.

Leads a front-end, back-end, or full-stack software development team, overseeing people management, coaching, delivery planning, technical quality, release readiness, operational support, and continuous improvement. Partners with Product and cross-functional teams to deliver secure, scalable software. Establishes Agile, testing, CI/CD, and AI-assisted development practices while managing technical debt, modernization, performance, and responsible AI adoption.

Build and ship full-stack features within small engineering Pods using Node.js, React, Golang, MongoDB, Elasticsearch, and AWS. Develop APIs, front-end interfaces, integrations, tests, and AI-powered capabilities. Collaborate with product, design, and senior engineers; participate in code reviews, production support, on-call rotations, and bug triage while growing technical, UX, and system-design skills.

Lead or assist customer implementations, collect and analyze customer requirements to design features and prioritize roadmaps, run online product demos, and provide technical/business support via phone, email, and support tickets.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.