Top Tech Jobs & Startup Jobs in Los Angeles, CA

Build and own the technical roadmap: design AI-first interfaces and APIs, architect scalable ETL pipelines, develop data models for ML features, ship autonomous B2B workflows (PO generation, vendor sync, schedule optimizations), and establish engineering standards, CI/CD, and mentoring.

Architects FHIR-compliant clinical data models and develops harmonization, enrichment, and validation strategies across EHR, HIE, and claims data. Maintains clinical ontologies and value sets, ensures data accuracy and longitudinal integrity, and documents technical requirements for engineering teams and stakeholders. The role requires advanced Python and SQL skills, healthcare interoperability expertise, and experience with clinical informatics or data integration.

Own corporate FP&A forecasting, consolidations, cash flow planning, reporting, and financial modeling. Partner with Accounting and cross-functional leaders to analyze variances, improve forecast accuracy, support executive and Board reporting, and guide resource allocation. The role also enhances planning processes, automation, and models covering stock-based compensation, capital expenditures, collections, share count, and long-range planning.

Lead end-to-end AI improvement initiatives by assessing workflows, identifying automation opportunities, gathering stakeholder requirements, designing and integrating AI-powered solutions, and managing deployment and adoption. The role also includes process redesign, executive communication, internal AI enablement, responsible AI governance, privacy, access controls, and measuring post-implementation business impact.

Build and optimize scalable Databricks data pipelines across a medallion architecture. Develop data models, implement data quality checks and validation, integrate Azure services, and ensure data accuracy, governance, lineage, security, and performance. The role requires designing config-driven pipelines supporting CDC and event-based processing, maintaining CI/CD deployments, and working with structured and unstructured data across SQL and NoSQL systems.

Drive partner health, product adoption, onboarding, training, and long-term engagement for cybersecurity clients and partners. Provide technical support, prepare partner reviews, document processes, collaborate with GTM and Product teams, and track customer health and KPIs using ChurnZero. The role requires customer-facing technical expertise, security-industry experience, strong communication, analytical skills, and independent remote work capability.

Own end-to-end Lyric deployment workstreams from planning through go-live. Manage project plans, meetings, timelines, budgets, risks, UAT, customer adoption, training, and post-launch monitoring. Partner with executives, engineers, and supply chain operators to translate business needs into platform solutions and ensure successful delivery.

Lead the architecture and delivery of modern data and analytics platforms for clients. Design governed, secure solutions using Microsoft Fabric, Azure, Databricks, Power BI, and Purview. Translate business needs into scalable architectures, estimates, roadmaps, and delivery plans. Guide architecture reviews, governance, CI/CD, testing, monitoring, and operational readiness while enabling AI, Copilot, RAG, and intelligent application initiatives. Serve as the technical lead and trusted advisor for client engagements.

Own the end-to-end customer experience for a product category across phone, SMS/chat, and email. Resolve customer issues, identify recurring problems, analyze patterns, and recommend systemic improvements. Serve as the product and customer-experience authority, collaborate cross-functionally, and improve satisfaction, response times, accuracy, and operational processes.

Design, build, and operate large-scale data platforms and production ETL/ELT pipelines. Partner with data science, analytics, and business teams; manage cross-functional delivery, QA, observability, data quality, experimentation infrastructure, and statistical model validation. Deploy containerized workloads through Docker, Kubernetes, CI/CD, and Helm. Own data operations, monitoring, incident response, SLAs, runbooks, and pipeline reliability while delivering accurate, scalable, and cost-efficient data assets.