Top Remote Data & Analytics Jobs

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Analyze complex datasets to identify trends, patterns, anomalies, and business opportunities. Develop KPIs, perform root-cause analysis, build Power BI dashboards using DAX, SQL, and Power Query, validate data quality, automate reporting, and translate findings into actionable recommendations for business stakeholders.

Conduct online research, gather and verify information from multiple sources, evaluate source reliability, and prepare structured notes, summaries, and reports. Identify organizations, professionals, publications, and industry data while maintaining accurate source records. The role requires clear written English, strong attention to detail, analytical thinking, consistent documentation, and independent time management.

Own the data and insights portfolio for Gainbridge, prioritizing business problems that analytics and AI can solve. Translate data and models into decisions, drive adoption within workflows, build data literacy, and govern applied AI use. Partner with data and engineering teams on use cases while retaining accountability for business outcomes, prioritization, acceptance, and stakeholder alignment.

Develops business cases, financial and operational models, data models, executive decision packages, portfolio dashboards, and benefit-validation reporting for cross-functional transformation initiatives. Extracts and analyzes enterprise data using SQL, Python, spreadsheets, and visualization platforms; automates repeatable workflows; standardizes metrics; tracks value realization; and partners with Finance, IT, data engineering, and business leaders to support decisions and embed sustainable reporting processes.

Administer and implement enterprise database platforms across Google Cloud and other cloud environments. Manage SQL Server, BigQuery, Cloud SQL, security, backups, monitoring, performance tuning, query optimization, schema design, data pipelines, cost optimization, and governance. Provide technical support, troubleshoot incidents, manage releases and database-as-code, respond to on-call issues, and collaborate with IT and application teams. Improve operations through automation and AI tools while documenting solutions and supporting users.

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Designs, implements, and maintains database solutions across multiple platforms. Analyzes and resolves database issues, reviews and optimizes SQL code, develops indexing and performance-tuning strategies, researches database structures, and implements ETL and reporting solutions using SQL Server SSIS and SSRS. Requires DB2 z/OS and SQL Server expertise, strong communication, documentation, analytical skills, and the ability to manage multiple projects.

Conduct quantitative research for traders by testing market hypotheses, building and maintaining documented signals, performing event studies and backtests, and validating results against leakage, survivorship bias, transaction costs, and regime dependence. Organize reproducible point-in-time datasets, communicate findings clearly, track signal performance, and use AI tools to accelerate research and coding while verifying outputs.