Top Senior Data & Analytics Jobs

Leads a team of data analysts while delivering business intelligence insights across Marketing, Product, Operations, and Finance. Owns the Tableau environment, including governance, permissions, licensing, data quality, and platform capabilities. Develops dashboards, KPI frameworks, and analytics roadmaps; oversees marketing attribution and campaign measurement; partners with Data Engineering on data availability; and drives stakeholder adoption of analytics products.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Build and operate data platform services, APIs, SDKs, schemas, validation tools, event-driven pipelines, and reverse ETL workflows. Improve Kafka-based data delivery, schema evolution, observability, lineage, access controls, privacy, and compliance. Partner with engineering, analytics, data science, security, and infrastructure teams to create reliable, self-service data workflows. Operate AWS services, infrastructure as code, monitoring, alerting, and incident response.

Leads analytics strategy and technical delivery for clients, specializing in Adobe Analytics and Customer Journey Analytics. Conducts measurement workshops, defines KPI and implementation strategies, oversees tagging and data quality, guides experimentation, and reviews dashboards and insights. Advises senior stakeholders, manages and mentors consultants, supports proposals and account growth, and contributes to Adobe Experience Platform initiatives involving Target, RT-CDP, and AJO.

Develops Power BI dashboards, leads data migration to a new fundraising and ERP system, validates and reconciles data, gathers reporting requirements, builds Power Automate workflows, documents processes, supports staff, and coordinates third-party data integrations. The role requires strong analytics, business intelligence, SQL, data modeling, reporting, and stakeholder communication skills, with approximately five years of experience.

Build and maintain scalable dbt data models, transforming raw data into trusted, documented datasets. Own warehouse data modeling and metric definitions, establish testing and code review standards, monitor data quality, optimize query performance, and troubleshoot reporting discrepancies. Partner with engineers, analysts, and business stakeholders to translate business needs into reliable pipelines. Mentor junior engineers and contribute to technical best practices and tooling decisions.

Own commercial analytics for direct-to-consumer and marketplace channels, analyzing pricing, promotions, conversion funnels, merchandising, product mix, customer behavior, and channel economics. Build scalable reporting tools, support forecasting and planning, and translate transactional and behavioral data into recommendations for digital commerce leadership. Partner with Marketing Analytics and Analytics Engineering to connect acquisition trends with commercial outcomes and ensure trusted data.

Lead and develop an analytics engineering team, define the analytics roadmap, and establish scalable standards for data modeling, business intelligence, and AI-enabled analytics. Partner with leadership, Operations, Engineering, and Product to solve complex business problems, deliver actionable insights, optimize data pipelines, and build a trusted data foundation. Champion documentation, data literacy, and evidence-based decision-making across the organization.

Lead and grow Confido’s data engineering team, initially managing an analytics engineer while remaining hands-on with SQL, data models, pipelines, and code reviews. Own warehouse architecture, orchestration, tooling, data standards, monitoring, and metric definitions. Hire and develop engineers, prioritize the team’s roadmap, collaborate across departments, and improve data reliability, documentation, and reporting across the company.

Designs, builds, and maintains complex data systems, including scalable data infrastructure, batch and streaming pipelines, data models, data products, and cloud-based architectures. Develops automated deployment pipelines, improves data systems, and supports data analysis and advanced analytics. Collaborates with cross-functional teams to gather requirements and ensure robust, governed, and accessible data solutions.