Top Data & Analytics Jobs

Designs and delivers analytics solutions on Snowflake using dbt, SQL, and modern ELT practices. Responsibilities include data modeling, pipeline development, migrations, performance optimization, data quality, security, orchestration, CI/CD, and release management. Leads discovery and requirements gathering, translates business needs into analytics architectures, provides technical guidance, and communicates recommendations to client stakeholders in agile consulting environments.

Build and manage high-volume analytics and production ETL/ELT systems. Partner with Data Science, Engineering, and Product teams to ensure reliable Search data logging and usage. Own data quality, observability, security, compliance, SLAs, scalability, performance, and cost efficiency across the data lifecycle. The role requires expertise in Python, SQL, data modeling, distributed data technologies, massive datasets, and data pipeline optimization.

Administers and optimizes the HRIS, including configuration, upgrades, integrations, troubleshooting, and data governance. Develops standard and ad hoc reports, KPI dashboards, regulatory reports, and employee survey outputs. Analyzes workforce data to identify trends, recommend improvements, and support strategic decisions. Partners with vendors and IT on system enhancements while ensuring data integrity, privacy, and operational efficiency.

Leads Gartner research and thought leadership on data management, governance, and AI. Develops actionable insights, analyzes market trends, advises executive clients, delivers presentations, supports sales and conferences, reviews content, improves research processes, and mentors analysts. Requires deep expertise in data and analytics, AI implementation, data security, data value creation, executive communication, research, and strategic analysis. Remote U.S. role with travel up to 25%.

Build and operate analytics pipelines, data models, data marts, and quality systems for mobile free-to-play game telemetry. Maintain and optimize BigQuery transformation layers, migrate Airflow pipelines to a partner-managed ETL platform, define tracking plans and data contracts, and develop observability capabilities. Partner with studios and analysts to support retention, monetization, and engagement reporting while applying production engineering, documentation, and collaboration practices.

Design and operate end-to-end infrastructure for intracortical neural data, from ingestion and validation through preprocessing, versioned dataset releases, and training data loaders. Preserve scientific metadata and provenance while implementing signal processing, quality checks, storage optimization, parallel processing, and monitoring. Collaborate with neuroscience, machine learning, infrastructure, and IT teams to support scalable, reliable model training and sensitive research-data management.

Manages and prepares data from internal and external sources, validates data quality, performs descriptive, predictive, and prescriptive analysis, and documents methodologies and outcomes. Develops and maintains self-service reporting tools and dashboards, supports regular and ad hoc reporting, trains customers, and presents audience-appropriate findings. Identifies anomalies, investigates root causes, ensures reproducibility, and provides recommendations to support data-driven underwriting and actuarial decisions.

Build scalable data foundations, analytics tooling, analysis pipelines, and trusted self-service datasets for product and data science teams. The role focuses on instrumentation, experimentation standards, data quality, metric governance, documentation, workflow orchestration, and cross-functional collaboration. Engineers will use SQL, Python or R, and AI-assisted workflows while validating results and protecting sensitive data.

Build and maintain IICS ETL/ELT pipelines, develop Amazon Redshift data models, and use SQL and Python for data transformation and automation. Create Power BI reporting models, implement data quality checks, document pipelines, and collaborate with client stakeholders and engineering teams. The role requires autonomous work, strong communication, and the ability to translate technical solutions into business context, with bonus value for Veeva or life sciences experience.

Develop, maintain, and support scalable data systems and pipelines for analytics and reporting. Collect, clean, structure, enrich, and govern data from multiple sources while ensuring quality, security, reliability, compliance, and performance. Collaborate with analysts, business analysts, and data scientists to design solutions, deploy optimized pipelines, troubleshoot data issues, and provide technical support.