Top Data & Analytics Jobs

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.

Owns fraud and market-abuse detection models, rules, and partner-facing surveillance reporting across trading data. Investigates wash trading, collusion, hedging, reverse trading, and other suspicious activity; calibrates thresholds, validates false positives, maintains data quality, and produces evidence-based findings. Requires strong SQL, cloud data warehouse experience, futures and derivatives knowledge, anomaly detection expertise, and the ability to communicate findings to partners and executives.

Supports Cencora’s enterprise data and analytics platform modernization by collecting and analyzing datasets, creating dashboards with BI tools, tracking KPIs, conducting ad hoc analyses, documenting findings, and contributing to CI/CD and self-service solution delivery. Collaborates with cross-functional stakeholders to provide actionable insights, improve processes, and support data-driven decisions for internal and customer-facing products.

Design and govern enterprise data architectures that make customer data secure, trustworthy, and AI-ready. Lead data modeling, ETL/ELT, integrations across cloud, ERP, CRM, and legacy systems, vector and retrieval data foundations, data quality, lineage, and compliance. Develop reusable architecture frameworks, evaluate tooling, mentor engineers, advise senior customers, and provide platform feedback. Support multiple concurrent forward-deployed customer engagements involving agentic AI, RAG, and LLM/SLM workflows.

Own analytics engineering for the machine infrastructure supply chain team. Build and maintain semantic layers, trusted data models, metrics, dashboards, and analytics-ready datasets using dbt and modern data platforms. Analyze inventory trends, optimize stock levels, investigate discrepancies, and develop supply chain KPIs. Partner with data engineering, finance, product, operations, planners, and buyers while improving inventory planning, replenishment, ERP processes, and warehouse efficiency.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.

Participate in a rotational analyst development program focused on using data and technology to analyze performance trends, identify business opportunities, and support insurance operations. Complete training, mentorship, and rotational assignments while developing strategic, technical, analytical, communication, and business skills. Use tools such as Excel, SQL, Python, R, SAS, Snowflake, Power BI, and emerging AI technologies to generate business insights.

Build and own scalable finance and revenue data pipelines, models, and datasets supporting ARR, NRR, bookings, billing, and revenue attribution. Ensure data quality, reliability, monitoring, reconciliation, and self-service access while partnering with Finance, Revenue Operations, Analytics, Product, and Engineering.

Analyze Experian portfolio repayment performance, credit risk, underwriting and credit-limit changes, collections, funnel metrics, cohort LTV, and unit economics. Own partner analytics models, data definitions, reporting, alerting, and data quality monitoring. Collaborate with Product, Engineering, Partnerships, Finance, and Experian to guide roadmaps, evaluate experiments, identify risks, and support business reviews.