Top Senior Data & Analytics Jobs

Analyze marketing and digital marketing performance across social, web, app, and email channels. Develop segmentation, attribution, funnel, conversion, retention, KPI, dashboard, and competitive analyses. Design A/B and multivariate experiments, identify data issues, and translate statistical findings into actionable recommendations for stakeholders and senior leaders. Collaborate with technical teams to improve data reliability and support consumer acquisition, retention, and product performance.

Designs, develops, and optimizes sales reporting, data models, pipelines, and dashboards (Power BI, SQL, Excel). Leads Oracle ERP reporting initiatives, data governance, automation, and cross-functional analytics to deliver actionable commercial insights and mentor analysts.

Leads a team of data engineers and owns Oxford’s Azure and Microsoft Fabric data platform strategy. Responsibilities include building and consolidating Fabric capabilities, developing and optimizing T-SQL and stored procedure-based ETL, orchestrating pipelines, managing reliability and performance, implementing data quality and CI/CD standards, and partnering with technical and business stakeholders to deliver governed, scalable data products.

Designs, builds, and operates scalable cloud data platforms across Azure, Microsoft Fabric, and AWS. Develops Python ETL/ELT pipelines, containerized Azure Container Apps Jobs, data warehouses, dimensional models, and integrations between on-premises and cloud systems. Responsibilities include infrastructure as code, CI/CD, monitoring, security, cost optimization, documentation, stakeholder collaboration, and mentoring junior engineers.

Designs, develops, and maintains research and operational databases, data models, ETL workflows, dashboards, data collection tools, and research websites. Administers secure applications and storage servers, manages permissions and data access under VA security requirements, performs data quality assurance, installs statistical applications, and supports and trains end users. The role also coordinates data governance, documentation, integration, modeling, and analysis across UCSF, VA, and research partners.

Own product analytics and measurement strategy, including KPI definition, experimentation, reporting, executive dashboards, and scalable dbt datasets. Partner with Product, Growth, Engineering, Design, and leadership to guide roadmap and marketplace growth decisions. Conduct product and market analysis, manage technology-category data, research cybersecurity and AI markets, and use LLMs to improve workflows and enrich the product database.

Leads the full lifecycle of business analysis across Field, Accounting, Sales, Operations, and Technology teams. Defines requirements, business cases, ROI analyses, process improvements, data-quality standards, and solution impacts. Supports software testing, issue resolution, systems analysis, analytics, and strategic decision-making. The role also researches technology trends, presents recommendations to management, and mentors entry-level business analysts.

Develops scalable Power BI and Microsoft Fabric solutions, including dashboards, semantic models, data pipelines, and lakehouse architectures. Partners with stakeholders to define metrics, gather requirements, improve data quality, and deliver governed analytics. Uses SQL, Python, PySpark, DAX, and Power Query to transform and visualize enterprise data. Supports deployment, automation, user training, troubleshooting, and adoption. The role requires experience with Dynamics 365 Finance and Operations, corporate reporting, and preferably CPG or food manufacturing environments.

Leads enterprise data strategy, analytics, business intelligence, data engineering, governance, and AI-enablement initiatives. Manages data and analytics teams, defines strategic roadmaps, develops executive dashboards and KPI frameworks, guides data warehouse and lakehouse architecture, and delivers advanced analyses, forecasting, and predictive insights. Partners with executives to improve operational efficiency, customer experience, revenue, profitability, and organizational performance while establishing scalable, governed, and reliable data capabilities.

As a Revenue Analytics Manager, you will own reporting for Sales, Marketing, and Customer Success, build dashboards, automate workflows, and ensure data quality.