Top Data & Analytics Jobs

Manages vendor master data compliance, including reviewing and approving vendor records, validating banking and tax information, maintaining documentation, conducting third-party checks, auditing ERP enrichment, and supporting audit readiness. Partners with vendors, financial institutions, internal teams, and data management support to resolve discrepancies and ensure accurate, compliant vendor data. Provides training and subject-matter expertise while meeting processing timelines, segregation-of-duties requirements, and service-level agreements.

Build AI agents and data acquisition systems, evaluate dataset quality, define quality KPIs, and improve data processes. Partner with product teams to deploy data into customer-facing products and acquire datasets from third parties, including coordinating vendors and contracts. The role requires organizing multiple projects, structuring analyses, communicating tradeoffs, and using data to support investment decisions and customer outcomes.

Analyze product and healthcare billing data to support product decisions, reporting, experimentation, and claims collection improvements. Build and maintain dashboards, develop scalable datasets, conduct A/B testing, create data visualizations, and communicate actionable insights. Collaborate with product stakeholders, data scientists, and engineers on data modeling, OKRs, data quality, and pipeline reliability for Billing and Payments features.

Own Toast’s Voice of the Customer program across customer segments, analyzing NPS, CSAT, CES, qualitative feedback, and business metrics. Identify root causes of customer pain, develop data-driven recommendations, improve CX measurement practices, and influence cross-functional teams to prioritize customer-focused solutions. The role also builds listening channels, promotes customer-centric decision-making, and aligns CX priorities with organizational goals.

Analyzes business performance, trends, competitor filings, and insurance product data for assigned states or product lines. Develops actionable recommendations, advanced queries, analytical tools, and strategic initiatives. Leads workstreams and low- to moderate-complexity projects, presents findings to product managers, supports countrywide implementations, and identifies improvements to property and specialty insurance offerings.

Analyzes customer behavior, marketing performance, and business trends to deliver actionable insights. Builds predictive models, forecasts, dashboards, data pipelines, and self-service analytics solutions. Designs experiments, performs statistical and hypothesis testing, evaluates attribution and customer metrics, and communicates findings through concise presentations to stakeholders and leadership. Collaborates across marketing, partnerships, finance, sales, product, engineering, and machine learning teams to support strategic decision-making.

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.

Supports private-market document processing, data-quality reconciliation, exception triage, workflow management, and offshore team coordination. Serves as a subject-matter expert on private-market data, analyzes trends, documents operational logic, identifies process improvements, and collaborates with Product, Data, and Engineering teams on roadmap and systemic fixes. Provides operational summaries and data-quality reports to support client-facing teams.

Build, deploy, monitor, and support data pipelines for connected devices, web applications, and mobile applications. Collaborate with internal customers, AWS Cloud Architects, and agile teams to develop scalable serverless data solutions using Python, SQL, Snowflake, Databricks, and AWS technologies. The role includes full lifecycle pipeline management, production support, technology evaluation, and participation in data product development under the supervision of a Senior Data Engineer.