Top Senior Data & Analytics Jobs

Own analytics for Customer Success and Operations by translating ambiguous business questions into metrics, reports, dashboards, and PRDs. Partner with senior leaders, CSMs, Analytics Engineers, and Operations teams to improve retention, permit turnaround, and organizational scalability. Build self-service dashboards, develop dbt models, document analytics assets, prioritize high-impact requests, and use LLM tools to accelerate SQL, reporting, and data-model development.

Build and maintain Snowflake and dbt data platforms, including ELT pipelines, tested models, orchestration, monitoring, documentation, access controls, and governance. Optimize performance and costs while ensuring reliable, discoverable data for internal dashboards, client products, and AI/ML use cases. Translate ambiguous stakeholder needs into durable solutions and apply strong software engineering practices, including Git, CI/CD, code review, Python, testing, and alerting.

Lead complex quantitative and qualitative consumer research projects for SEGA’s game portfolio from study design through analysis, synthesis, reporting, and stakeholder readouts. Partner with Product, Brand, Corporate Planning, and Publishing teams to translate business questions into actionable insights. Manage research roadmaps and budgets, ensure methodological quality, align stakeholders, and mentor junior researchers. Research areas include market sizing, segmentation, concept testing, playtests, pricing, messaging, brand health, and product tracking.

Lead the design, implementation, maintenance, security, migration, and optimization of highly available PostgreSQL and Amazon Aurora databases on AWS. Develop data warehousing and ETL solutions, manage scheduling and monitoring, tune performance, support database projects, and maintain technical documentation. The role supports Army Human Resources Command by improving HR processes and decision-making through modern cloud database technologies.

Own customer experience and delivery metrics, dashboards, and reporting across Support, Professional Services, Customer Success, and Renewals. Analyze customer, operational, usage, and financial data to identify risks, trends, bottlenecks, and renewal threats. Partner with leaders to define KPIs, translate business questions into recommendations, validate data quality, automate reporting, and build self-service analytics. Collaborate with AI teams to measure the impact of AI-driven tools.

Establish and operationalize Finance data governance across Finance and Technology. Define governance models, business glossaries, data inventories, lineage, quality controls, master-data standards, hierarchies, and reconciliation processes. Support SAP, Snowflake, reporting, planning, and analytics data products; facilitate stakeholder workshops; document requirements; manage governance backlogs; and coach data owners and stewards in an ambiguous enterprise environment.

Owns the design, development, deployment, and support of B2B data pipelines using BigQuery, dbt, Python, and orchestration frameworks. Translates client requirements into scalable data models, integrates CRM and marketing data, builds measurement and activation pipelines, and ensures quality through testing, validation, monitoring, and observability. The role also standardizes reusable B2B data capabilities, troubleshoots production issues, and supports account-level marketing-to-revenue measurement across multiple clients.

Leads RentSpree’s Data organization across data engineering, data science, analytics, and strategy. Owns the data and ML roadmap, analytics self-service, experimentation standards, governance, data quality, vendor strategy, and GTM data foundation. Manages analytics leaders and partners with engineering, product, finance, executives, and the Board to drive data-informed strategy. The role also scales globally distributed teams, develops talent, and remains hands-on enough to review SQL, models, metrics, and warehouse architecture.

Develop and maintain data engineering tools, services, ETL pipelines, ingestion processes, data transformations, extraction tools, and enterprise data models. Analyze structured and unstructured source data, optimize query performance, process large data volumes, collaborate with data scientists, and provide project documentation.

Own enterprise data platform architecture, modeling, pipelines, testing, and documentation using technologies such as AWS Redshift and dbt. Translate complex data into actionable commercial insights and client growth strategies. Lead executive client relationships, roadmap discussions, KPI definition, QBRs, and consulting delivery. Manage and develop a sizeable cross-functional team across offices and time zones while aligning data governance, analytics, marketing, engineering, and CI/CD practices.