Top Data & Analytics Jobs

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

Leads advanced analytics strategy and delivery, including data preparation, statistical and machine learning model development, experimentation, validation, pricing analysis, and presentation of actionable insights. Partners with business stakeholders to identify opportunities, ensures analytical quality and automation, and builds and manages a high-performing analytics team. Requires leadership experience, advanced quantitative methods, large-scale database work, and proficiency with SQL, SAS, R, Python, and related tools.

Leads the New York analytics function and team while shaping company-wide measurement strategy. Oversees reporting standardization, cross-account measurement infrastructure, analytics capability development, experimentation, dashboarding, partner relationships, and client-facing insights. Drives analytics integration into business growth opportunities, develops team talent, and presents complex measurement recommendations to executive stakeholders. Requires extensive media analytics leadership and expertise in advanced measurement methodologies, databases, advertising channels, research partners, and verification tools.

Analyze, clean, validate, and visualize structured and unstructured data supporting military capability modernization. Develop data methodologies and pipelines, assess data quality, identify trends, establish metrics and KPIs, and present findings to clients and senior Joint Force stakeholders. Use Python, R, SQL, Palantir Foundry or Maven Smart Systems, Microsoft tools, Tableau, Qlik, and Power BI to produce insights, recommendations, documentation, and cross-functional solutions.

Own and build the company’s data layer, including metadata-driven schemas, semantic modeling, versioned query interfaces, data lineage, and auditable change tracking. Write production code, establish engineering and AI-first practices, coach engineers, and collaborate with executive and engineering leadership on architectural direction. Experience with multi-tenant SaaS and clinical data standards is advantageous.

Develops cloud-based data solutions for precision marketing to healthcare professionals in a commercial biopharma environment. The role integrates, cleanses, structures, analyzes, reconciles, and visualizes internal and external datasets, while operationalizing data science models. It collaborates with brand representatives, marketers, data scientists, and omnichannel strategists to define requirements and deliver reliable data products for business stakeholders and senior management.

Design, build, deploy, and maintain scalable cloud-native data platforms, pipelines, lakes, warehouses, and analytics solutions. Support data science, AI/ML, and business intelligence initiatives while processing structured and unstructured data. Lead collaborative teams, guide analysts and developers, assess cloud architectures, and support migration and modernization efforts for mission-driven clients.

Lead design and implementation of enterprise fraud prevention strategy and decisioning engines for loan products. Use data analytics and machine learning to assess fraud and identity risk, recommend verification standards, set KPIs, report to senior leadership, and lead/mentor a team to automate and improve fraud prevention while maintaining a frictionless customer experience.

Establish and lead the People Analytics function, building trusted workforce data foundations, governance, privacy standards, analytical products, workforce planning capabilities, and AI-enabled decision support. Partner across HR, Finance, IT, and business leadership to deliver executive insights, improve workforce decisions, and scale analytics operations. Recruit and develop the analytics team while ensuring responsible data use, global-local compliance, stakeholder adoption, and alignment with organizational priorities.

Leads Pacific Life’s Operations Data Analytics function, setting strategy, governance, roadmaps, standards, and resource plans. Oversees reporting, dashboards, KPI standardization, data modeling, forecasting, predictive analytics, and executive insights. Partners with Operations Transformation, Technology, Finance, Risk, and business leaders to improve efficiency, customer experience, quality, and financial performance. Manages senior analytics professionals, capacity planning, prioritization, budgets, stakeholder adoption, data quality, and continuous improvement.

Build, optimize, and maintain scalable ETL pipelines and data warehousing solutions using Azure technologies. Integrate complex datasets, manage database quality and security, automate processes, document data architectures, and collaborate with data scientists, analysts, and stakeholders. The role also requires experience with SQL, programming languages, artificial intelligence, machine learning, and related analytical methods.

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.

Senior Business Intelligence Engineer responsible for designing Account 360 Medallion Architecture, integrating enterprise and telemetry data, building predictive models and enrichment pipelines, and delivering actionable insights. The role uses Python, R, SQL, machine learning, NLP, and process mining to analyze device usage, customer workflows, and product friction. Responsibilities include rapid data-product deployment, stakeholder collaboration, sprint-based workload management, cross-training, technical debt reduction, and up to 15% travel.

Build analytical frameworks, measurement methodologies, and decision support to drive enterprise decisions across marketing, loyalty, pricing, menu, media, and operations. Mine and integrate customer, transaction, digital, and operational data; develop segmentation, predictive models, and dashboards; lead test-and-learn, incrementality, and forecasting efforts; translate analysis into clear business recommendations and scalable decisioning tools.

Designs, builds, deploys, and maintains scalable data platforms and ETL/ELT pipelines. Processes structured, unstructured, batch, streaming, and event-based data while optimizing data lake architecture and cloud performance. Collaborates with analysts, engineers, developers, and data consumers on mission-driven projects. Responsibilities include data modeling, governance, cataloging, lifecycle management, automation, platform tuning, and developing data solutions for applications such as fraud detection, cancer research, and national intelligence.

Two-year early-career rotational program with three cross-functional assignments focused on data engineering and analytics. Participants create analytical reports, work with machine learning and artificial intelligence, lead intensive business projects, and develop technical, interpersonal, and leadership skills. The program provides exposure to the full data lifecycle, business strategy, and continuous improvement, with potential placement into a post-program role.

Leads marketing analytics and end-to-end measurement across growth marketing, including dashboards, pipeline attribution, ROI, CAC, LTV, forecasting, budget analysis, experimentation, and data quality. Partners with Marketing, Finance, RevOps, and BI teams to connect spend with pipeline and revenue. Uses AI, machine learning, predictive modeling, and automation to improve reporting, attribution accuracy, insight generation, and investment decisions.

Leads enterprise risk dashboards, semantic models, data pipelines, and predictive and descriptive analytics using Power BI and SQL. Partners with IT, finance, audit, compliance, and operations teams to ensure data quality, governance, security, and regulatory compliance. Translates complex risk and audit datasets into actionable insights for senior leadership while establishing responsible AI, analytics, and data warehouse governance practices.

Administer and secure DCIM database systems (Nlyte Neo), collect/ingest and clean data center equipment/cabling data, build scripts, complex views, reports and Power BI dashboards to visualize power/space/cooling/cabinet utilization, tune performance, document designs and processes, assist software releases, design cloud data solutions, and support enterprise data center portfolio management for mission-critical operations.

Leads enterprise business intelligence strategy, roadmap, governance, analytics, data engineering, data science, and AI-enabled automation. Partners with executives across functions to define KPIs, deliver dashboards and predictive insights, oversee data platforms and integrations, manage technology vendors and budgets, and lead a global team of BI engineers, data engineers, analysts, and data scientists.

Conduct quantitative research for a global, mid-frequency statistical arbitrage portfolio. Responsibilities include exploring alternative datasets, developing trading signals, improving portfolio construction, risk and transaction cost models, applying machine learning and data science techniques, writing production-quality code, and managing research projects from idea generation through implementation.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Maintain data holdings and data standards, analyze complex geospatial datasets, transform data, create visualizations, and provide recommendations to improve data strategies and machine learning model development.

Analyze sales, marketing, and operational data to create dashboards, reports, predictive models, and business insights. Lead data projects, build data models and pipelines, maintain reporting accuracy, support governance and quality initiatives, and communicate findings to nontechnical stakeholders. Collaborate with leadership and cross-functional teams to improve performance, forecasting, and operational efficiency.

Lead advanced risk, fraud, and payment optimization analyses for major fintech merchants. Analyze large-scale transactional, ML output, and labeled risk data; design experiments and statistical investigations; develop scalable analytical tools, ETL workflows, dashboards, and reusable frameworks; collaborate with risk product teams; translate findings into merchant strategies and product improvements; enable commercial stakeholders through playbooks and self-service tools; and mentor analysts.