Top Tech Jobs & Startup Jobs in Seattle, WA

Collects, validates, audits, and improves proprietary private-market data using internal research tools, databases, business intelligence reports, and Power BI. The role supports data quality, customer requests, team training, process improvement, cross-functional collaboration, and coordination with the Mumbai Data Operations team. It requires strong communication, organization, analytical judgment, attention to detail, and interest in financial markets.

Leads Salesforce administration and a team of CRM administrators, overseeing platform operations, configuration, automation, integrations, releases, governance, data quality, security, and user adoption. Partners with Revenue Operations, Marketing, Sales, Customer Success, IT, vendors, and product teams to translate business needs into scalable GTM system solutions. Manages intake, backlog, support SLAs, deployment processes, platform reliability, and continuous improvement while remaining hands-on with Salesforce and related tools.

Develop and operationalize machine learning systems powering AI-driven insights from structured and unstructured financial data. Build NLP, generative AI, LLM, summarization, semantic search, classification, and prediction solutions. Responsibilities include model architecture, training, deployment, monitoring, optimization, scalable data pipelines, technical reviews, mentoring, research translation, and cross-functional collaboration with engineering, product, and data teams.

Develop and deploy production-grade machine learning systems that generate insights from structured and unstructured financial data. Responsibilities include NLP, summarization, semantic search, classification, prediction, GenAI, and LLM development; model training, monitoring, optimization, and operationalization; scalable data pipeline integration; architectural contributions; and collaboration with engineering, product, and data science teams.

Design, build, and maintain backend services and large-scale data pipelines processing billions of records. Own components end-to-end from requirements and architecture through production support. Develop scalable Java microservices, contribute to technical decisions, review code, troubleshoot production systems, and collaborate with engineering, product, and data science teams. Apply AI tooling and LLM capabilities where relevant while supporting reliability, testing, and continuous improvement.

Manages APAC Data Operations teams and strategy, including hiring, training, people development, data quality assurance, research methodologies, benchmarking, budgeting, and operational reviews. Leads strategic initiatives and multi-year data expansion roadmaps while partnering with Sales, Customer Success, Product, and Engineering. Uses SQL, Python, BI tools, automation, and AI-enabled research tools to improve workflows, track KPIs, address data gaps, and enhance client support.

The Systems Administrator designs, configures, and maintains company infrastructure, including cloud systems, networks, firewalls, and SaaS tools. Responsibilities include escalated support, access administration, vendor coordination, infrastructure troubleshooting, and leading migrations, software rollouts, and cross-functional IT projects. The role also supports SOC 2 audit evidence and controls, documents IT processes, and pilots AI-driven service improvements. This is an onsite position requiring collaboration with distributed teams and occasional travel.

Leads the Enterprise Data Platform team, managing data engineers and driving the data engineering roadmap. Oversees scalable pipelines, ETL/ELT workflows, cloud architectures, data models, governance, quality, lineage, and performance optimization. Partners with technology and business leaders to deliver enterprise data solutions and AI/ML capabilities. Responsibilities include architecture decisions, team development, mentoring, stakeholder collaboration, compliance, and operational improvement.

Manage research data products and methodologies, build and maintain SQL and Python data pipelines, optimize queries, and support production deployments. Partner with Product, Engineering, Data Operations, and analyst teams to implement methodology updates, data cleanups, and internal or production applications. Improve research workflows, communicate project changes, and mentor junior engineers while supporting financial market research outputs.

Lead a small engineering team while being a hands-on backend contributor: design and build ingestion pipelines, entity resolution, enrichment workflows, and search indexing. Own service quality, performance, reliability, observability, and estimations. Mentor engineers, drive technical practices, reduce technical debt, and collaborate with product and partner teams. Use cloud, containerization, streaming systems, and search technologies to deliver scalable, production-grade systems.