Top Data & Analytics Jobs

Build a canonical entity and knowledge graph for identity resolution, permissions, compliance, and agent authorization. Develop production data pipelines across internal systems, graph-based correlation, ML matching, schemas, APIs, provenance, bitemporal models, drift detection, and delegation credentials. Partner with Finance and Compliance on classification pilots while enforcing privacy, regulatory compliance, jurisdictional partitioning, and auditability.

Supports Technology and Digital Quality initiatives through business process and data analysis, data ingestion, statistical analysis, reporting, dashboard development, automation, and AI validation. The intern prepares datasets, identifies insights, develops recommendations, documents processes, and communicates findings to technical and non-technical stakeholders. The role collaborates with quality, finance, operations, and other departments while contributing to continuous improvement and data-driven decision-making.

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Designs and maintains data architecture, ETL/ELT pipelines, data warehouses, lakes, and workflow automation. Ensures data quality, governance, security, availability, and performance while supporting reporting, analytics, dashboards, and machine learning. Translates business needs into technical data models, monitors data systems, and collaborates with Technology, Analytics, and business stakeholders. The role is based in the administrative office with work-from-home eligibility; travel is not required.

Leads analytics for B2B sales, partnerships, revenue operations, and marketing teams. Responsibilities include forecasting, opportunity sizing, partner performance analysis, enterprise market intelligence, RFP analytics, lead scoring, funnel and lifecycle optimization, predictive modeling, experimentation, and executive insight generation. The role partners with Data Science and Engineering teams to improve revenue growth, onboarding, retention, campaign effectiveness, and sales performance across enterprise and SMB channels.

Supports production planning and digital transformation by analyzing manufacturing, inventory, scheduling, and production-performance data. Builds dashboards, reports, and visualizations; documents workflows; identifies automation and AI opportunities; assists with analytics and digital-tool pilots; validates data; researches emerging technologies; and presents recommendations to operations, planning, and leadership stakeholders in a manufacturing environment.

Build and maintain production-grade data models while migrating workflows from Alteryx to dbt. Manage Dagster or Airflow pipelines in Redshift, create Tableau dashboards, and translate technical data into RevOps insights. Partner with Sales, Marketing, and Customer Experience leaders on KPI reporting, ABM, and customer health scoring. Explore AI, LLM, predictive analytics, and SageMaker machine learning applications including churn prediction and lead scoring.

Build and maintain dbt data models, migrate Alteryx workflows, and manage Dagster or Airflow pipelines in Redshift. Partner with Revenue Operations stakeholders to translate data into business insights, dashboards, and KPI visibility using Tableau. Apply AI, LLMs, and machine learning through SageMaker to support predictive use cases such as churn prediction and lead scoring.

Build and maintain dbt data models while migrating workflows from Alteryx, managing Dagster or Airflow pipelines, and supporting Redshift analytics. Partner with RevOps stakeholders to translate data into business insights, dashboards, KPIs, ABM initiatives, and customer health scoring. Explore LLMs, predictive analytics, and SageMaker machine learning for use cases such as churn prediction and lead scoring.

Build and migrate production data models from Alteryx to dbt, manage Redshift pipelines with Dagster or Airflow, and deliver stakeholder-facing Tableau insights for Revenue Operations. Translate technical data into business impact while supporting ABM, customer health scoring, predictive analytics, churn prediction, lead scoring, and AI/LLM initiatives using machine learning and Amazon SageMaker.

The Director leads the Data & Analytics department, setting strategy, architecture, governance, and a multi-year roadmap. Responsibilities include overseeing data pipelines, analytics, visualization, AI/ML applications, data quality, risk controls, and self-service solutions. The role manages stakeholder engagement, executive reporting, vendor partnerships, and cross-functional delivery while recruiting, developing, and retaining a high-performing multidisciplinary team.

Summer Analyst internship for students pursuing a bachelor’s or graduate degree. Quantitative strategists apply mathematics, programming, logical reasoning, research, and analytical methods to construct quantitative models supporting risk, financial controls, compliance, treasury, and other core financial-market processes. Interns receive training, work on real responsibilities, and collaborate with experts in quantitative finance and engineering.

Nine- to ten-week summer associate internship for advanced-degree students. Quantitative strategists apply mathematics, programming, logical reasoning, research, and analytical methods to construct quantitative models supporting financial markets and firm-wide risk, compliance, treasury, and reporting processes. Participants receive training, collaborate with experienced professionals, and work on real responsibilities using modern engineering, cloud, AI, and machine learning capabilities.

Summer Analyst internship for students pursuing a bachelor’s or graduate degree. Quantitative strategists apply advanced mathematics, programming, logical reasoning, research, and analytical methods to construct quantitative models supporting global financial markets and core financial operations. Interns receive training, attend orientation, and work on real responsibilities alongside experienced professionals in a fast-paced environment.

Leads enterprise data analysis, integration, governance, visualization, business intelligence, and advanced analytics initiatives. Develops scalable ETL/ELT pipelines, dashboards, predictive insights, and data quality controls across multiple platforms. Partners with business and technology stakeholders, provides technical leadership, mentors analysts, supports cloud analytics modernization, and contributes to data strategy and architecture decisions.

Leads enterprise analytics initiatives, advanced statistical analysis, predictive modeling, business intelligence, data integration, governance, and AI-enabled solutions. Advises senior leaders, establishes data strategies and analytical roadmaps, oversees dashboards and architectures, evaluates modern technologies, resolves complex data challenges, and mentors analytics professionals.

The Director of Data owns the company’s data platform, governed metrics, client identity resolution, data quality, privacy controls, and AI-ready datasets. This leader manages the data team, establishes governance and operating standards, oversees warehouse and pipeline architecture, and ensures reliable data for Finance, leadership, products, and ML/LLM systems. The role requires hands-on technical leadership, roadmap ownership, stakeholder communication, and accountability for data reliability, lineage, security, and platform costs.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Leads claims-focused insights, analytics, business intelligence, and AI initiatives for an insurance organization. Partners with Claims, Reserving, Actuarial, Finance, Underwriting, and Transformation teams to translate business priorities into data products, dashboards, predictive insights, automation, and AI capabilities. Manages and develops an analytics team, ensures measurable business value, promotes responsible AI adoption, and maintains data, AI, and information governance standards.

This internship develops quantitative analytics and financial engineering solutions for BlackRock’s Aladdin platform. Responsibilities include building financial risk models, applying quantitative techniques to investment and client-business problems, developing Python-based solutions, delivering actionable analytics, and collaborating with investment and business teams. Interns gain experience in financial markets, investment products, quantitative research, technology, and the Aladdin platform.

Participate in BlackRock’s quantitative investing internship across quantitative research, portfolio management, product strategy, and investment technology. Apply statistical models, machine learning, Python programming, and large-scale data analysis to investment strategies across asset classes. Learn about capital markets, portfolio trading, economic theory, client objectives, and investment product development while collaborating with research, technology, strategy, and investment professionals.

Develop and optimize Pacsun’s enterprise MicroStrategy environment, including schemas, semantic layers, dashboards, dossiers, reports, and performance tuning. Integrate generative AI, natural-language queries, predictive analytics, anomaly detection, and RAG-enabled use cases. Write complex Snowflake SQL, support ETL/ELT processes, enforce governance and security, troubleshoot platform issues, document solutions, collaborate with business stakeholders, and mentor junior developers.

Analyze large and complex datasets to identify trends, clean and transform data, query databases with SQL, and develop dashboards and visualizations using Databricks, Tableau, Power BI, and Excel. Produce analytical reports and recommendations, apply predictive analytics when appropriate, and support agile delivery through user story documentation and testing.

The Senior Analytics Engineer builds and maintains data models, ETL pipelines, analytics products, business intelligence solutions, and visualizations. The role partners with stakeholders, Data Governance, Data Science, and Data Platform teams to define metrics, improve data quality, support AI and machine learning initiatives, and establish software engineering practices. Additional responsibilities include DataOps, workflow orchestration, technical documentation, business advising, and emergency response support. The position requires advanced SQL and Python skills, data engineering experience, and travel up to 10%.

Apply statistical and analytical methods to identify revenue opportunities, build predictive and explanatory models, design and evaluate experiments, analyze and integrate data sources, and translate findings into actionable recommendations for cross-functional stakeholders across hotel, F&B, pool, spa, and gaming operations.