Top Tech Jobs & Startup Jobs

Build and maintain scalable batch and real-time data pipelines and services for finance systems. Develop ETL workflows, data models, APIs, and distributed processing solutions using Spark, cloud platforms, and SQL. Ensure data governance, security, and quality while collaborating with engineers, product teams, and business stakeholders to troubleshoot, optimize, and improve system reliability.

Design, build, deploy, and maintain production-grade AI/ML applications (including LLMs and agentic systems). Implement APIs, services, data pipelines, testing, monitoring, and CI/CD. Collaborate with data scientists, platform, security, and product teams to deliver secure, scalable, observable AI solutions that create measurable business value.

Lead full-stack engineering for production-grade AI applications, including user experiences, APIs, backend services, integrations, and data-driven workflows. Drive architecture, system design, security, scalability, testing, deployment, observability, and operational support. Collaborate with AI/ML engineers, data scientists, product partners, and business stakeholders to deliver practical AI-powered solutions. Provide technical leadership, mentor engineers, resolve implementation risks, establish reusable engineering patterns, and maintain high standards for reliable, accessible, and maintainable applications.

Lead the architecture, deployment, management, and optimization of Google Cloud infrastructure, with a focus on networking, automation, security, observability, and reliability. Develop cloud-native platform services and tooling in Go, manage infrastructure through Infrastructure as Code, establish engineering standards, evaluate technologies, lead testing and implementation, and mitigate operational risks. Partner across engineering, architecture, product, and platform teams to deliver scalable, secure, and cost-effective systems.

Reconcile, review, and resolve balance sheet GL accounts; ensure month-end close accuracy; maintain controls and documentation; prepare metrics and reports; drive process improvements; analyze issues and lead stakeholder meetings to resolve discrepancies.

Lead and support UX research across Target's digital and in-store products. Partner with product, design, engineering, and analytics to scope, run, and synthesize qualitative and quantitative studies (usability testing, surveys, ethnographies, intercepts). Manage multiple projects, translate insights into compelling stories to inform product and strategy, and maintain the research repository.

Own Target’s on-platform agentic experience, defining product vision, strategy, roadmap, OKRs, and foundational capabilities. Identify high-value guest problems, prioritize use cases, and lead delivery with UX, Engineering, Data Science, Personalization, platform teams, and business partners. Balance experimentation with scalable, trusted experiences while aligning dependencies, managing the backlog, and influencing stakeholders across the organization.

Lead and perform manual and automated application- and network-layer penetration tests, triage bug-bounty reports, document and risk-rate findings, verify remediation, collaborate with third-party security teams, and ensure compliance with frameworks like PCI DSS and SOC.

Leads the design, development, deployment, and operation of production-grade full-stack applications integrating AI/ML, LLMs, retrieval-augmented generation, and agentic systems. Builds frontend experiences, backend services, APIs, integrations, and data workflows while guiding architecture, security, reliability, testing, observability, and operational readiness. Partners with AI/ML engineers, data scientists, product teams, security, and business stakeholders; mentors engineers and establishes reusable engineering practices.

The Senior Data Engineer is accountable for leading a global team, developing code, maintaining data sets, and implementing BI solutions using best practices in data modeling and ETL/ELT processes.