Top Data & Analytics Jobs

Leads enterprise analytics, reporting, data engineering, and data platform teams. Sets strategy, manages managers and technical teams, drives modernization and roadmap execution, and partners with business and technology leaders. Oversees scalable, secure, governed analytics solutions using Microsoft and Databricks technologies while establishing standards for data management, reporting, operational support, and self-service analytics. Also supports AI-enabled capabilities, vendor relationships, budgets, and high-performing team development.

Lead data ownership and governance to deliver curated, reusable data products. Partner across business, technology, analytics, and risk to define metadata, lineage, quality rules, and lifecycle management. Drive data integration, mitigate data risks, enable analytics/ML use cases, automate governance with AI/LLMs, and manage delivery, remediation, and stakeholder relationships to ensure scalable, discoverable, and enterprise-fit data products.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.

Leads Major League Soccer’s Marketing Science function, overseeing teams and external resources while developing marketing analytics strategy. Partners with Marketing, CRM, Technology, Product, and Data Engineering teams to improve audience targeting, personalization, campaign measurement, media investment, and fan growth. Oversees predictive modeling, AI and machine learning applications, media mix modeling, experimentation, attribution, and CRM activation. Communicates analytical insights and strategic recommendations to executive stakeholders across League and Club organizations.

Performs land-cover data production, quality assurance, remote-sensing and GIS analysis, and processing of raster and vector datasets. Responsibilities include working with imagery, LiDAR, elevation data, map services, metadata, automated GIS workflows, and derivative products such as maps, statistics, StoryMaps, and web applications. Supports NOAA coastal management projects independently and collaboratively.

Curate, cleanse, validate, and maintain large US equity and options datasets; integrate fundamental and alternative data; build data pipelines for research and trading; perform statistical and ML analysis to identify market patterns; document data provenance and collaborate with engineers and traders to deploy production-grade models.

Analyzes global market microstructure, trading algorithms, execution data, and performance across equity and FX markets. Develops execution analytics, reporting frameworks, trading signals, and machine learning models to improve trading strategies. Advises execution traders, evaluates vendor tools and data, automates workflows, and supports quantitative research projects. The role requires strong programming, statistical, AI and machine learning capabilities, familiarity with financial markets, and flexibility across trading hours.

Leads marketing analytics, predictive modeling, segmentation, and experimentation to improve acquisition, retention, and pipeline performance. Designs and maintains machine learning models, governs A/B and multivariate testing, translates insights into campaign execution, and evaluates analytics platforms. Manages budgets, regulatory compliance, strategic initiatives, and team performance while partnering with Finance, Sales, Operations, IT, and servicing teams. The role is fully on-site with occasional adjusted schedules.

Build and maintain Palantir Foundry data pipelines, ontologies, applications, workflows, and decision-support capabilities for defense missions. Translate stakeholder problems into technical solutions, integrate structured and unstructured data, troubleshoot production systems, and collaborate directly with clients through deployment. Apply engineering best practices including testing, version control, and documentation while communicating with technical and non-technical audiences. Travel to client sites may be required.

Designs, builds, deploys, and operates data architectures and software for capturing, managing, storing, and using structured and unstructured data. Establishes data pipelines and storage processes across cloud and local environments, develops automated AI and machine-learning tools for data transformation and integrity, creates development standards, and recommends system and storage improvements based on business and product requirements.

Conduct quantitative research on equities market data by identifying predictive signals, building and backtesting statistical and machine learning models, and translating findings into trading strategies. The intern will work with traders and senior researchers, maintain large-scale market data pipelines, and refine research prototypes using backtest results and feedback.

Lead quantitative analyses to design, implement, and evaluate Medicare and Medicaid payment models. Query large claims/enrollment datasets, build payment calculations, financial and quality metrics, and apply ML/econometric/predictive models. Mentor staff, write reproducible code and documentation, develop client relationships, perform rapid ad-hoc analyses, and help integrate AI into technical workflows.

Manage multiple oil and gas data products focused on natural gas pipelines, corridor flows, and prices. Build and maintain data models, pipelines, automated workflows, and programmatic data acquisition processes using Python and SQL. Monitor industry developments, market dynamics, key companies, and regulators to support accurate datasets and knowledgeable client assistance. Collaborate across teams, respond to user feedback, and improve data products and processing workflows.

Supports AI and cloud solution development by extracting, transforming, standardizing, and integrating text, JSON, geospatial, and other datasets. Assists with automated data ingestion, data services, pipelines, and machine learning algorithm integration for testing and operational deployment. Coordinates data requirements with project managers, customers, vendors, software engineers, architects, and data teams while managing changing priorities in a national security environment.

Designs, develops, and optimizes scalable Azure-based data engineering solutions, including lakehouses, warehouses, ETL/ELT pipelines, Databricks notebooks, and analytics workflows. The role supports enterprise reporting, AI and machine learning initiatives, feature engineering, Generative AI, and MLOps. Responsibilities include performance tuning, CI/CD automation, production support, technical design, lifecycle management, stakeholder collaboration, and translating business requirements into secure, reliable data platforms.

Defines and evolves enterprise data architecture supporting analytics, reporting, governance, integration, and AI capabilities. Designs scalable Lakehouse and Medallion architectures, data models, contracts, APIs, and event-driven integrations. Establishes governance standards and trusted data products while aligning platform investments with business and regulatory needs. Leads modernization initiatives, develops data strategy and roadmaps, supports KPI frameworks, mentors technical talent, and guides distributed engineering teams.

Designs and owns analytics-ready datasets, semantic views, ELT pipelines, and standardized metrics in Snowflake. Partners with business, analytics, and engineering teams to enable self-service BI, maintain data quality and documentation, and support forecasting, machine learning, and generative AI initiatives. Provides senior guidance on data modeling, analytics engineering practices, and scalable data product design.

Provides executive leadership for enterprise data and analytics strategy across clinical, operational, financial, quality, and population health domains. Leads data governance, stewardship, quality, AI and machine learning initiatives, analytics platforms, reporting, and data architecture partnerships. Builds and manages multidisciplinary analytics teams, promotes data literacy, ensures regulatory compliance, and delivers trusted insights for strategic and operational decisions.

Summer business intelligence internship analyzing digital and financial data for mobile banking and online banking experiences. Responsibilities include exploratory analysis, SQL or Python coding, trend analysis, data documentation, statistical and machine learning applications, and presenting data-driven insights. Interns collaborate with business intelligence and data science teams while participating in Agile/Kanban project activities.

Develop and administer enterprise business intelligence solutions using Power BI, SSRS, SQL Server, and data warehousing technologies. Design data models, dashboards, visualizations, KPI scorecards, security models, and analytical solutions for large-scale datasets. Optimize queries, ensure data quality and governance, establish BI standards, and provide guidance to other developers. Collaborate with engineers, database administrators, and business partners to translate complex data into actionable insights.

Conduct quantitative research for high-frequency trading strategies across global markets. Responsibilities include processing data, generating signals, building statistical and machine learning models, simulating and calibrating strategies, and developing research pipelines and simulation environments. The role requires collaboration with experienced traders and researchers to deliver measurable trading performance and well-documented production-quality code.

Leads the enterprise analytics organization, managing Analytics Engineers, Data Analysts, and BI Developers. Defines analytics strategy; delivers executive dashboards, KPI scorecards, operational reporting, semantic models, and self-service BI. Establishes data governance and metric standards, ensures data quality, partners with Engineering, Product, Finance, Operations, and executives, and translates business questions into actionable analytical solutions. Mentors analytics talent and drives prioritization, adoption, and measurable business outcomes.

Design and develop cloud-based data platform solutions, including data pipelines, ingestion, transformation, cleansing, integrations, and data applications. Translate technical requirements into detailed designs, write reliable code, develop test automation, troubleshoot operational issues, tune batch and real-time processing, secure cloud data platforms, and implement CI/CD and monitoring. The role also involves advising client developers and defining efficient operational processes.

Design and deploy ML and NLP models (including LLM evaluation) to predict borrower risk and property valuations, advise business teams, mentor junior analysts, and foster data-driven experimentation.