Top Data & Analytics Jobs

Leads enterprise performance and analytics strategy, including Business Intelligence, executive dashboards, KPI governance, reporting automation, data standards, and decision support. Partners with Finance, IT, and business leaders to improve forecasting, planning, operational performance, and value creation. Oversees Power BI and Microsoft Fabric initiatives, enterprise reporting architecture, mergers and acquisitions analytics, Board reporting, and self-service reporting. Supervises and develops analytics personnel while promoting enterprise-wide data-driven decision-making.

Leads advanced data analytics, statistical modeling, predictive analytics, and business intelligence for supply chain and logistics operations. Analyzes complex datasets, develops forecasting and optimization models, creates Tableau or Power BI dashboards, monitors KPIs, and translates findings into recommendations for government stakeholders. Provides technical leadership, quality oversight, and mentorship to junior analysts.

Develops and maintains insurance pricing and predictive models, analyzes premiums, losses, claims, and exposure data, evaluates profitability and emerging risks, supports rate filings and actuarial analyses, and delivers recommendations to leadership. The role also ensures data integrity, prepares financial reporting, automates workflows, and collaborates with product, actuarial, and business teams to improve pricing strategies and operational efficiency.

Designs, builds, and leads enterprise data and AI platforms across Azure and Databricks. Develops scalable batch and streaming pipelines with Azure Data Factory, Databricks, and Apache Spark; implements DevSecOps, CI/CD, infrastructure as code, security, governance, data quality, and metadata standards. Supports analytics, machine learning, and AI workloads while mentoring engineering teams and partnering with stakeholders on enterprise data outcomes.

Develop and maintain BI dashboards and ETL pipelines, transform and integrate structured and unstructured data, build analytical models and forecasts, apply machine learning and distributed computing (Databricks/Spark), create actionable visualizations (Qlik/Tableau/Power BI), and present insights to leadership while working in a cleared defense environment.

Lead licensing analytics for Disney Consumer Products, supporting Retail and Marketing through performance analysis, consumer insights, market trends, dashboards, and strategic recommendations. Translate business needs into data requirements, curate structured and unstructured data, build automated pipelines, develop self-service reporting, and improve data governance and architecture. Lead cross-functional analytics projects, communicate complex findings clearly, and guide technical transformation involving visualization, AI, and process automation.

Analyze and optimize e-commerce pricing strategies using sales, demand, customer behavior, competitive pricing, AI, and experimentation. Develop pricing models, measure elasticity, evaluate revenue and margin impacts, monitor competitor prices, and build dashboards, alerts, and scenario analyses. Translate analytical findings into recommendations and partner with Ecommerce, Marketing, Product, and Finance teams to improve profitable growth.

Leads advanced people analytics using Workday as the foundation for workforce insights, planning, retention, compensation, and talent decisions. Develops analytical datasets, metrics, dashboards, predictive models, and executive reporting; establishes data definitions, governance, quality controls, and architecture standards. Partners with HR Technology, Finance, HR leaders, and business executives to translate complex workforce data into strategic recommendations. Serves as a Workday and people-data subject-matter expert, mentors analysts, and influences enterprise decisions through clear storytelling.

Leads Magellan Health’s enterprise data and analytics strategy, modernization, engineering, governance, business intelligence, and AI/ML enablement. Oversees data platforms, pipelines, quality, lineage, compliance, and reporting while partnering with clinical, operational, and financial stakeholders. Builds and develops a 30+ person organization, manages a $3M–$5M budget, oversees vendors, and presents investment cases to executive leadership. Drives transformation from a low-maturity environment to a governed, insight-driven enterprise data platform.

Develop data visualizations, interactive dashboards, and data pipelines to transform complex structured and unstructured datasets into actionable insights. Analyze data quality, relationships, and types using Python, R, or SQL, and work in Foundry to build or maintain pipelines. Collaborate with business stakeholders to clarify reporting needs and communicate findings. The role may support DoD and Army data systems and requires eligibility for a Secret clearance.

Develop and test mathematical models for sports betting pricing and risk management. Collect, clean, and analyze sports and betting datasets to identify trends and inform trading strategies. Use Python, C#, R, or C++ to build quantitative models and automate analysis. Collaborate with senior quants, traders, engineers, and product managers, document methodologies, and communicate findings. This hybrid role requires working two to five days weekly in the Denver office.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

The Data Engineer will design and build data pipelines and platforms, mentor teams, and implement AI/ML solutions for various high-impact projects, primarily in secure environments.

Data Engineer Interns will build and support data pipelines, workflows, transformations, and quality processes for analytics, reporting, automation, and AI initiatives. They will work with structured and unstructured data, cloud platforms, databases, and engineering tools while collaborating with cross-functional teams. The internship provides mentorship, Agile team exposure, and hands-on experience applying Python, SQL, ETL, data modeling, and emerging AI technologies to insurance business needs.

Clean, standardize, enrich, and import product data into the PIM while maintaining data integrity and governance standards. Oversee bulk ingestion processes, leverage AI and automation, support data model improvements, resolve product listing issues, and contribute to data hygiene and workflow modernization. The role also involves collaborating with client-facing teams, documenting processes, proofreading product data, communicating progress, and adapting to changing priorities.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Build data extraction, transformation, loading, modelling, analytics, machine learning, and dashboard solutions for teaching and learning data. Partner with faculty to gather requirements, generate insights, analyze student learning behavior, and develop Power BI visualizations. Train and advise lecturers on dashboard use and provide knowledge transfer on analytics and modelling.

Analyze large-scale transactional, operational, and customer datasets; build visualizations, machine learning models, and experiments; translate complex findings into clear recommendations for operational teams and executives; and apply analytical software, databases, visualization tools, and programming languages in a fast-paced blockchain environment.

Leads enterprise master data governance across customer, product, supplier, and transaction domains. Establishes data standards, architecture, quality metrics, systems of record, and warehouse structures. Partners with IT, analytics, and business leaders to enable reliable reporting, AI, customer segmentation, pricing, sourcing, and operational insights. Drives data cleanup, enrichment, continuous improvement, cross-functional governance, training, and data literacy while maintaining business ownership and accountability for enterprise information integrity.

Leads enterprise master data governance across customer, product, supplier, and transaction domains. Establishes data standards, architecture, quality metrics, systems of record, and governance processes; partners with IT, analytics, and business leaders to enable reliable reporting, AI, customer segmentation, sales growth, sourcing, pricing, and operational decisions. Oversees data enrichment, architecture approvals, corrective actions, change management, data literacy, and cross-functional implementation of enterprise information standards.

Entry-level role supporting data, analytics, technology and transformation projects. Participate in classroom training, complete assigned projects, follow coding and documentation best practices, build data-driven presentations, and analyze data to support decision-making while collaborating across Data-Analytics, Technology, and Transformation teams.

Build and maintain scalable web crawlers to extract, validate, and structure product data from eCommerce sites. Use AI-driven techniques, browser automation, proxies, and APIs to handle dynamic content and anti-bot measures. Monitor performance, clean large datasets, collaborate with Product/Engineering/Data teams, and continuously improve crawling frameworks and automation.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Officer, Model Risk Management validates bank-wide models, ensuring compliance with regulations. Responsibilities include developing testing plans, assessing model performance, reviewing documentation, and collaborating with stakeholders for effective model governance.