Top Senior Data & Analytics Jobs

Design and maintain scalable data models, batch and streaming ETL pipelines, and end-to-end data workflows supporting business intelligence, machine learning, and operations. Ensure data quality, consistency, governance, traceability, and reliable delivery across distributed systems. Partner with business teams, platform engineers, and product managers to translate requirements into documented solutions and resolve data issues. The role is based in Seattle with three days per week in the office.

Build Deepgram’s governed decision layer above warehouse gold tables. Responsibilities include modeling cross-system entities, owning semantic and metrics definitions, creating agent-facing data tools and MCP surfaces, implementing data quality assertions and reconciliation checks, evaluating AI agent accuracy, and answering business questions while leaving reusable models behind. The role requires production-grade SQL and Python, semantic-layer ownership, strong data modeling judgment, AI-native workflows, clear writing, and comfort building infrastructure from scratch.

Lead analytical investigations and build measurement frameworks, dashboards, reporting, and reusable data products for Customer Experience leadership. Partner with data scientists on customer health scoring, forecasting, segmentation, and validation. Use SQL and Python to turn ambiguous business questions into decision-ready insights, document solutions, and operationalize analytics across CX teams.

Analyze revenue performance, forecasting, business drivers, and market trends to support planning and commercial growth decisions. Partner with Sales, Finance, Business Intelligence, and other stakeholders to identify risks, opportunities, and actionable insights. Develop segmentation, opportunity-sizing, prioritization analyses, and investment business cases. Improve recurring reporting and planning processes through automation and scalable tools, translating complex data into clear recommendations for senior leadership.

Owns Help Desk analytics, reporting, dashboards, KPI scorecards, and operational intelligence for Technology Support Services. Analyzes service tickets, demand, performance, escalations, workforce utilization, vendor effectiveness, and customer experience. Produces executive reporting, conducts root cause analysis, supports forecasting and capacity planning, establishes service measurement frameworks, and leads data-driven continuous improvement initiatives. Partners with technology leaders, support teams, vendors, and business stakeholders to improve service quality and operational performance.

Lead analytics and reporting for alternative credit portfolios: monitor and analyze investment, portfolio and fund performance; produce monthly/quarterly/ad hoc client reports; maintain data integrity and reporting processes; support investor due diligence and custom requests; coordinate with valuation, fund accounting, investor relations, compliance and portfolio managers.

Leads data engineering and analytics engineering teams delivering production pipelines, curated data products, semantic models, dashboards, and governed Databricks lakehouse capabilities. Owns platform administration, data quality, observability, security, cost optimization, reliability, and engineering standards. Manages internal and external resources, mentors engineers, drives AI-assisted development, partners with stakeholders, and reports roadmap progress to leadership. Requires strong hands-on Databricks expertise, technical leadership, advanced SQL, data modeling, CI/CD, and up to 5% travel.

Manage and improve outbound and inbound dialer operations for personal loans, credit cards, and loan origination outreach. Monitor campaigns, agent productivity, dialer performance, and operational KPIs. Analyze data and build dashboards to recommend efficiency improvements. Ensure compliance with FDCPA, Reg F, TCPA, and state laws, maintain business controls, support remediation, and collaborate with IT, Telephony, Risk, auditors, and business stakeholders.

Own the architecture and standards of the company’s dbt project, building scalable models across staging, intermediate, and mart layers. Partner with product, design, and engineering teams to evolve data models, validate metrics, and maintain data quality. Develop AI-assisted testing, documentation, and access workflows; collaborate on Segment, Fivetran, and Airflow pipelines; and drive privacy, governance, and warehouse best practices.

Lead data analysis and modeling for Growth and Monetization, focusing on sales trials, product-led sales, pricing, packaging, and monetization. Partner cross-functionally to generate actionable insights, maintain dbt models and ELT pipelines, improve data workflows, evaluate emerging tools, and enable colleagues to use Hex effectively. The role requires strong analytical judgment, SQL and dbt expertise, and an interest in applying AI to data work.