Top Data & Analytics Jobs

Develop analytical solutions and actionable insights from customer, machine, technology, and utilization data. Identify adoption barriers and growth opportunities, quantify impacts on safety and productivity, define performance indicators, and collaborate with engineering, product, commercial, dealer, and field teams to build scalable analytics solutions and customer-engagement strategies.

As a PhD intern, you will develop and test trading strategies, perform statistical analysis, and collaborate with senior researchers.

Conduct quantitative research for automated trading strategies by developing mathematical models, translating algorithms into code, backtesting trading signals, analyzing unconventional data, and refining monetization systems. Collaborate with senior researchers during an 11-week post-doctoral internship while applying advanced probability, statistics, machine learning, and time-series techniques to financial markets.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Own end-to-end analytics engineering for AI and developer-experience telemetry, including instrumentation, production pipelines, governed metrics, monitoring, executive dashboards, and analysis. Define and validate new measures of AI productivity and ROI, communicate findings to senior stakeholders, and recommend improvements to tooling, processes, and investment. Collaborate with data engineering, applied AI, and data science teams while using AI coding agents to build and maintain pipelines and reporting.

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Participate in a three-year rotational development program across John Deere business areas. Apply data science, data engineering, statistical analysis, predictive modeling, visualization, and programming techniques to solve business problems. Manage large datasets, support analytics projects, collaborate with stakeholders, and build expertise in analytics technologies while completing three one-year assignments.

Designs, develops, tests, and maintains data architectures, ETL pipelines, databases, and large-scale processing systems. Builds scalable data infrastructure, improves data delivery processes, and ensures data quality, integrity, availability, and performance. Collaborates with data scientists and architects while troubleshooting pipelines and systems. The role requires a technical bachelor’s degree, at least two years of data engineering experience, U.S. citizenship, and knowledge of SQL, data warehousing, and data integration.

Owns data governance and lifecycle management for Wealth Management. Defines data requirements, metadata, classifiers, quality standards, and risk processes; maintains a data-product backlog; investigates data issues; partners with technology and business teams; supports data publishing and governance initiatives; and mentors junior staff.

Owns data governance and management procedures supporting business operations and advanced analytics. Defines data requirements, metadata, classifiers, and quality standards; manages data-product backlogs; monitors lifecycle risks; investigates data issues; and partners with technology and business teams on remediation and compliance. The role also supports data publishing standards, risk management, and junior staff development.

Leads development and operation of secure, scalable data platforms, pipelines, and data products. Designs AWS-based data engineering solutions using Python, Spark, Databricks, and related services; develops semantic layers with Iceberg and APIs; and implements CI/CD with Terraform. Responsibilities include code development and review, troubleshooting, access-control evaluation, resiliency, automation, and enterprise AI-assisted workflows. Collaborates within agile teams to support large-scale analytical and operational data use cases.

Supports the enterprise data governance program by developing policies, enabling data ownership and stewardship, governing metadata and data quality, coordinating compliance activities, facilitating stakeholder engagement, tracking governance initiatives, and reporting KPIs. The role promotes adoption of data catalogs, business glossaries, data standards, privacy requirements, retention controls, and governance best practices across business and technology teams.

Build Affirm’s narrative intelligence function by analyzing audience, behavioral, brand, media, survey, and business data. Develop narrative measurement frameworks, dashboards, analytical standards, and feedback loops to assess how effectively communications resonate. Design rigorous research studies, integrate qualitative and quantitative evidence, identify meaningful patterns, and translate findings into positioning, messaging, campaign, content, and communications strategy. Partner across analytics, research, marketing, creative, and communications teams to turn complex evidence into actionable decisions.

Designs and maintains reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, deployment automation, cloud integrations, containers, and infrastructure as code. Partners with data consumers on usable models and datasets, documents standards and runbooks, supports Agile improvement, and responds to critical IT incidents. Requires 5+ years of data engineering experience, orchestration, distributed processing, storage, scripting, automation, and version control expertise.

Designs and implements reusable data ingestion and transformation components, automated quality and governance checks, reliable pipelines, monitoring, alerting, and observability practices. Supports CI/CD, cloud integrations, container orchestration, infrastructure as code, deployment automation, data modeling, documentation, and Agile platform operations. The role also contributes to incident response and critical escalations.