Top Hybrid Data & Analytics Jobs

Supports Fund Management through regulatory communications, cost allocation, operational reporting, and analytics. Develops and maintains Access, Excel, SQL, and enterprise reporting solutions; integrates data across systems; improves data quality and workflows; and supports testing, validation, deployment, automation, and AI-enabled process enhancements. The role also serves as a technical subject matter expert and coordinates regulatory reporting while driving efficiency and cost savings.

Own growth and product analytics across activation, retention, adoption, and expansion. Analyze pricing, packaging, monetization, cohorts, and experiments; build instrumentation, measurement infrastructure, AI-assisted scripts, agents, and automation; and turn insights into shipped product or growth changes. Partner with growth, product, pricing, and engineering while establishing data quality standards and trusted metric documentation.

Analyze transaction enrichment product performance, data quality, accuracy, match rates, and other metrics. Investigate anomalies in large datasets, build dashboards and automated alerts, improve QA processes and workflows, and develop documentation and monitoring standards. Partner with Product, Engineering, and AI/ML Data Science teams to translate insights into product improvements and automate recurring analytical work.

Maintains Baan databases, master files, label databases, prices, costs, part numbers, bills of materials, routings, and related manufacturing data. Audits database accuracy, documents changes, supports engineering changes and label updates, assists with quality procedures, and trains functional teams on Baan and associated databases. The role requires strong attention to detail, independent prioritization, database knowledge, and familiarity with manufacturing processes and ISO/IATF systems.

Analyzes large-scale datasets to generate strategic insights, design and evaluate experiments, identify trends, and improve business performance. Builds scalable reporting frameworks and dashboards, translates complex analyses into compelling narratives, and partners with cross-functional teams to support strategic initiatives and data-driven decisions across casino products and markets.

The Senior Data Platform Engineer will design, maintain, and enhance Hinge's data platform, including data pipelines, monitoring systems, and user feedback incorporation while ensuring data quality and system reliability.

Build and maintain dbt data models and pipelines, implement real-time ingestion with PySpark and AWS Glue, and process distributed data in Databricks. Apply software engineering practices, review Git pull requests, partner with data scientists and stakeholders, translate requirements into Jira stories, and support data governance, quality, integrations, and deployment across the enterprise.

Leads a team of five experienced analysts and data scientists while personally owning strategic analytics across ecommerce, health, supply chain, inventory, fulfillment, and merchandising. Partners with executives, establishes KPI and reporting standards, prioritizes the analytics roadmap, and delivers actionable business insights. The role also advances AI-native, self-service analytics capabilities, strengthens data governance and quality, and builds a high-performing analytics culture.

Develop business intelligence and revenue reporting tools, automate Finance reporting processes, perform ad hoc and quantitative analyses, and translate complex data into actionable insights. The analyst will support company initiatives, present findings to technical and non-technical stakeholders, and provide decision-making guidance across the organization.

Serve as the primary technical escalation point for managed services data and analytics clients. Troubleshoot and enhance Power BI, Fabric, Azure, and other analytics environments; gather requirements; translate business needs into scalable solutions; support client onboarding; maintain documentation; mentor support staff; and partner with client success and consulting teams. The role also contributes to continuous improvement, training, knowledge sharing, and AI-enabled productivity initiatives.