Top Data & Analytics Jobs

Develop and maintain Python data pipelines to produce business data objects supporting applications. Build and deploy CI/CD pipelines, implement source-to-target mapping as pipeline code, and perform unit and integration testing. Solve complex application problems, interpret design requirements, uphold software quality standards, and mentor team members. Work onsite in Chicago or Peoria, IL.

Design, develop, maintain, troubleshoot, and document scalable data pipelines and infrastructure using AWS, Airflow, Snowflake, Git, and Azure DevOps. Collaborate with analytics engineering teams to support One Funnel and OneSite initiatives and enable data-driven business decisions. Apply SQL and Python to solve business challenges, improve system reliability, implement CI/CD and automated testing, and manage data engineering requirements in an Agile environment.

Design, administer, and optimize enterprise data platforms (including Snowflake) to support analytics and business operations. Lead data architecture, integrations (SAP, PRISM, cloud), governance, security, and platform reliability. Partner with business and IT teams, manage cloud services (Azure, Kubernetes, Key Vault), DevOps workflows, and Linux/Windows environments while driving continuous improvement and onboarding analytics tools.

Supports client-facing digital assurance and technology projects by analyzing data, assisting with auditing and financial reporting processes, evaluating internal controls, and researching business transformation solutions. The role uses Python and Microsoft SQL Server Data Tools to generate insights, support regulatory compliance, and deliver objective assessments while collaborating with senior auditors and project teams.

Supports data analysis projects in PwC’s Advisory practice, researching datasets, identifying patterns, integrating data, and creating dashboards and visualizations with Alteryx and Power BI. Assists with algorithm development, analytics automation, and data-driven insights for clients. The internship emphasizes learning PwC methodologies, collaborating with experienced professionals, and gaining exposure to AI, cloud platform design, and data architecture.

Supports assurance teams with data, analytics, and technology-enabled projects focused on financial reporting transparency and regulatory compliance. Performs basic research, data wrangling, analysis, visualization, and process improvement using Alteryx. Collaborates with experienced auditors, learns assurance methodologies, contributes to client support initiatives, and develops professional communication and decision-making skills.

Extract, transform, validate, and load client and Enverus data into MineralSoft using AI-enabled extraction tools and ETL processes. Configure mappings, run queries and reports, troubleshoot integrations with EnergyLink, and ensure data quality and completeness for internal and client use.

Lead Prolaio’s enterprise data engineering strategy, architecture, pipelines, cloud data platforms, integrations, governance, and AI-enabled data capabilities for regulated healthcare applications. Build secure, scalable systems supporting clinical, device, patient-generated, research, analytics, and product data. Establish engineering standards for lineage, provenance, lifecycle management, privacy, HIPAA, and auditability. Hire and manage high-performing teams, influence cross-functional architecture decisions, and evaluate data platform vendors and tools.

Design, build, and manage scalable batch and real-time data pipelines, databases, and processing systems. Develop and administer Databricks and Snowflake environments, optimize data architecture and distributed processing, manage PostgreSQL and MySQL databases, and implement governance, monitoring, alerting, and troubleshooting practices. Collaborate with analytics, engineering, and product teams to deliver reliable data solutions, document workflows, and promote modern data engineering practices.

Build and operate scalable data pipelines and trustworthy data models supporting Layla’s AI-powered travel products. Own data quality, reliability, observability, experimentation, and model-evaluation foundations. Enable production AI/ML systems through feature and data availability, evaluation datasets, and monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights, establish engineering best practices, and mentor other engineers.

The Senior Data Analyst will define trusted product metrics, analyze funnels, cohorts, retention, and traveler behavior, and design A/B tests. The role builds dashboards and self-service reporting, partners with Product, Marketing, and Engineering, and communicates actionable insights that guide prioritization and product decisions. The analyst will also help establish analytics standards and apply AI-assisted tools responsibly within Layla’s travel-planning and booking products.

Define trusted product metrics, analyze funnels, cohorts, retention, and traveler behavior, and design A/B tests for Layla. Build dashboards and self-service reporting, partner with Product, Marketing, and Engineering, and translate analysis into actionable recommendations. Apply AI-driven tools and data concepts to improve product decisions and measure impact.

Develops regulatory compliance analytics and reporting solutions using SQL, dashboards, governed metrics, semantic layers, and AI-enabled business intelligence. Builds and maintains Tableau or similar dashboards, automates reporting with Tableau and Alteryx, evaluates Machine Learning and GenAI opportunities, supports prompt development, and translates stakeholder needs into analytical products. Collaborates with Compliance, Risk, Technology, Finance, and senior governance leaders to improve regulatory engagement insights, adoption, and firm-wide decision-making.

Lead enterprise efforts to define and enforce metadata, semantic, and data-readiness standards. Profile domains, surface and remediate quality/definition gaps, convert fixes into scalable patterns, build prototypes for analytics and AI use cases, and report readiness scorecards and KPIs to senior stakeholders.

Monitor fraud trends in retail deposit acquisition, analyze large datasets, identify defensive gaps, and develop strategies balancing fraud losses, operational costs, and customer impact. Partner with cross-functional and technical product teams to deliver fraud prevention capabilities, present actionable insights to senior leadership, and support business growth while protecting customers.

Leads the data engineering team, including hiring, coaching, technical direction, platform architecture, data pipelines, Snowflake warehousing, governance, quality, observability, and incident support. Partners with Data Science, Analytics, Clinical Operations, Biostatistics, and Business Development to deliver trusted data products for clinical operations, asset evaluation, reporting, and machine learning. Oversees budgeting, resourcing, documentation, technical programs, and responsible use of AI engineering tools.

Manage and validate merchant data integrations that feed Riskified’s data science models. Responsibilities include configuring data transformations, onboarding clients, assessing data flows, creating JSON and CSV templates, writing Python, SQL, Spark, Databricks, or R scripts, wrangling large datasets, monitoring data alerts, resolving data issues, and collaborating with engineering, product, data science, research, account management, and sales teams.

Leads third-party advertising measurement strategy, partner governance, benchmarking, meta-analyses, and analytics innovation. Advises senior executives, influences cross-functional roadmaps, develops measurement playbooks, presents industry thought leadership, and builds measurement capabilities across Sales, Product, Marketing, and Partnerships. The role requires advanced statistical and causal inference expertise, hands-on SQL and visualization skills, and more than ten years of advanced analytics and measurement experience.