Top Data & Analytics Jobs

Develop and automate data workflows, analytical tools, visualizations, and AI/ML capabilities supporting engineering, product design, sales, and manufacturing. Translate business requirements into automated solutions, monitor production datasets, improve analytics workflows, collaborate with IT and cross-functional teams, and communicate analytics value to stakeholders. The role also provides data expertise and documentation across the organization.

Leads data engineering, advanced analytics, and AI initiatives supporting engineering productivity and customer value. Translates engineering, manufacturing, and customer requirements into data tools, analytic workflows, visualizations, APIs, and scalable AI solutions. Oversees data monitoring, predictive and prescriptive analytics, agentic workflows, stakeholder engagement, agile delivery, and cross-functional projects while leading a small team and promoting documentation and knowledge sharing.

Leads marketing and sales analytics strategy, architecture, and measurement. Builds data models, pipelines, machine learning datasets, attribution and incrementality models, and revenue-performance analyses. Advises leaders on investments, tools, account prioritization, and funnel economics. Establishes data, metric, testing, and privacy standards; partners with Marketing, Sales, Finance, Revenue Operations, Data Engineering, and IT; plans roadmaps; presents recommendations to executives; and mentors analysts and engineers.

Leads enterprise business data strategy, governance, analytics, business intelligence, AI enablement, and data modernization. Partners with executives and cross-functional leaders to establish data ownership, improve quality, develop analytics and KPI frameworks, prioritize AI use cases, and measure transformation value. Builds data-focused organizational capabilities, drives adoption and data literacy, develops investment cases, and guides enterprise initiatives that improve forecasting, planning, operational decisions, and business outcomes.

Leads enterprise business data strategy, governance, analytics, business intelligence, AI enablement, and modernization initiatives. Establishes data ownership models, roadmaps, KPIs, and governance frameworks while partnering with executives and business functions. Builds data and analytics capabilities, develops investment cases, drives transformation value realization, promotes data literacy, and leads cross-functional change to improve decision-making and business outcomes.

Leads the Streaming Test Analytics team across five sites, overseeing AI-driven analytics, anomaly detection, software infrastructure, test data management, governance, and process simplification. Drives customer adoption, program execution, stakeholder alignment, employee development, recruiting, and operational handoffs. Requires technical leadership in machine learning, aircraft engine performance, Python or Java development, Agile CI/CD, and large-scale program management.

Leads Vizient’s indirect spend data and analytics strategy, operating model, governance, quality standards, reporting, automation, and AI adoption. Oversees data lifecycle management, modeling, integration, categorization, normalization, analytical methodologies, dashboards, and decision-support tools. Manages data and analytics teams, develops reusable assets for consulting engagements, partners with technology and product teams, and supports business development and member presentations.

Builds data ingestion, integration, transformation, modeling, and quality frameworks for DHS financial management programs. The role uses SQL, Python, R, PySpark, and Databricks to analyze large datasets, develop predictive and machine learning models, support data governance, and generate decision-making insights. Responsibilities also include client meetings, documentation, presentations, client service, and business development support.

Leads Duck Creek Technologies’ Intelligence function, overseeing analytics engineering, business intelligence, reporting, dashboards, metrics, and insight generation. Builds scalable analytics platforms and governance standards, partners with product, AI, engineering, and business leaders, and translates complex data into actionable recommendations. Manages analytics teams and advances enterprise data-driven decision-making, operational performance, and product development.

Build and maintain data infrastructure, software systems, ETL processes, analytics tools, and metrics for the Starlink Mobile network. Aggregate and analyze large datasets, detect application issues, fuse data from multiple sources, create data catalogs, and develop predictive models and machine learning pipelines. Collaborate with engineering teams, operators, and external partners to assess network growth and effectiveness.

Design, build, and maintain scalable data pipelines, warehouses/lakes, and platforms. Lead multidisciplinary teams to implement ETL/ELT (batch and streaming), optimize data lake performance, enforce data governance, and support analytics and ML use cases on cloud and on-prem environments.

Build and architect scalable cloud data platforms, pipelines, and applications for Capital One’s Risk Tech organization. Develop distributed data solutions using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Design secure, resilient, and operationally efficient data systems, including streaming and AI-enabled solutions. Collaborate with Agile teams, product managers, engineers, analysts, and data scientists while mentoring peers and communicating technical concepts to stakeholders.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.

Build and oversee scalable data platforms, warehouses, lakehouses, ETL pipelines, Spark workflows, and tracking architecture. Integrate data from disparate sources, implement governance and monitoring, optimize queries, and support KPI dashboards and business insights. Develop AI/ML capabilities within data pipelines, automate infrastructure with AWS, Terraform, and CI/CD, and collaborate across engineering, product, DBA, DevOps, and SecOps teams. Mentor engineers and analysts while delivering secure, accurate, terabyte-scale data solutions.

Build and maintain scalable data pipelines, platforms, and ETL/ELT workflows that organize structured and unstructured data. Develop scripts and data feeds, manipulate Elastic data, support reporting and analytics, and convert varied data into usable formats. Collaborate with analysts, engineers, developers, and data consumers in an agile environment while assessing, designing, monitoring, and operating data platforms for mission-driven clients.

Leads Marsh Re’s global data strategy and a multidisciplinary team of approximately 20 data engineers, scientists, developers, and product managers. Drives client-facing analytics advisory, scalable data and AI product development, enterprise data governance, cloud data architecture, machine learning, GenAI, MLOps, and responsible AI. Owns product lifecycle, commercialization, roadmap prioritization, executive reporting, team development, and collaboration with IT to industrialize innovation across the firm.

Conduct quantitative credit risk analytics, econometric modeling, financial forecasting, and concentration analysis. Develop scalable Python, Spark, Hadoop/HIVE, SQL, Tableau, and Alteryx frameworks; implement monitoring, diagnostics, and production support for risk models. Analyze large financial, market, macroeconomic, and alternative datasets using statistical and machine learning techniques to inform portfolio management, capital decisions, and senior leadership discussions.

Analyzes soccer event, tracking, physical, and performance data to support team decisions, player evaluation, recruitment, opposition analysis, and match reporting. Develops visualizations, metrics, models, reports, and reproducible Python workflows while answering ad-hoc questions and collaborating with sporting stakeholders. Requires strong soccer knowledge, quantitative reasoning, communication, and flexibility for match-related evenings and weekends.

Build and deploy scalable data pipelines and platforms that organize structured and unstructured data for mission-driven client projects. Collaborate with analysts, developers, data engineers, and data consumers in an agile environment. Support data assessment, exploration, examination, platform design, development, and maintenance. The role requires Palantir Foundry, Python, CI/CD, SQL, TS/SCI clearance, and travel to OCONUS locations up to 25% of the time.

Designs, builds, and deploys scalable data solutions using Palantir Foundry, React, TypeScript, PySpark, and backend technologies. Develops data pipelines, integrations, ontologies, APIs, production applications, and mission-critical workflows. Partners with analytics, application, engineering, program, and client teams to deliver unified enterprise solutions. The role also requires software engineering best practices, automated testing, CI/CD, clear communication, and independent ownership of deliverables.

Develop and maintain Power BI reports, dashboards, visualizations, semantic models, and analytics solutions. Translate business requirements into scalable technical solutions, optimize queries and data models, ensure reporting accuracy and performance, and support data governance. Serve as the primary liaison for business users, provide training and troubleshooting, manage tickets, and collaborate with Data and QA teams. The role also advances AI and machine learning capabilities, mentors colleagues, and contributes to automation, documentation, and code reviews.

Manages Image Recognition and Smart Sell applications, master data, data pipelines, databases, and Power BI dashboards. Ensures data quality and supports data preparation, transformation, analysis, and reporting. Partners with commercial stakeholders, regional IT teams, and vendors to translate business needs into practical technology and analytics solutions, resolve issues, and drive adoption.

Build data pipelines and relational data models using Python and SQL; clean operational data, support scenario analysis, and create dashboards for PMO leadership. Explore LLMs and machine learning for text analysis and data-quality automation. Gain production experience with testing, documentation, maintainable code, and data-driven decision support in a trading environment.

Analyze user growth, engagement, and revenue data to identify trends, develop actionable insights, and guide product strategy. Lead forecasting for user growth and engagement, support performance management, create dashboards and reporting, and improve analytics foundations with data science, product, and engineering partners. Present findings to senior leadership and contribute to product initiatives, metric development, and business operations.

Analyze and optimize the end-to-end sales funnel by developing KPIs, dashboards, diagnostic analyses, and operational measurement frameworks. Partner with Sales, Operations, Data Engineering, and Data Science teams to improve conversion, revenue, capacity planning, and pipeline prioritization. Lead AI-native automation initiatives, including LLM analytics assistants, automated reporting, prompt engineering, and machine learning model validation.