Top Remote Data & Analytics Jobs

The Senior Database Developer designs, optimizes, and maintains databases, migrating data, developing queries, and ensuring security standards are met. They collaborate with teams and support development in DB-related tasks.

Design, build, and maintain ETL/ELT data pipelines and domain-oriented data products to support a Digital Thread. Ensure data quality, governance, mapping, and discoverability. Apply software-engineering practices (version control, testing, CI/CD, IaC) for production deployments. Integrate manufacturing and operational systems and prepare AI/ML-ready datasets for analytics and predictive use cases.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

As a Sr. Data Engineer at Lyra Health, you will build and scale core data infrastructure, design data warehouses, and create efficient data pipelines while collaborating across departments.

As a Technical Solutions Manager, you'll onboard Enterprise customers onto Medallion's platform, focusing on data migration, integration, and automation for enhanced operational efficiency.

Lead modernization and operation of Dutch Bros' data platform: design, build, and maintain scalable Snowflake-based data pipelines and infrastructure, enable real-time ETL/ELT, support analytics and ML workflows, implement CI/CD and IaC, ensure data governance and security, and communicate technical solutions to stakeholders.

The Marketing Data Scientist will analyze data, onboard clients, develop data pipelines in R, and propose improvements to enhance efficiency.

Lead data strategy at Alpha, oversee data functions, build robust data systems, drive product decisions using data insights, and mentor a data team.

As a Data Scientist, you will analyze blockchain data to derive insights, develop and deploy predictive models, and improve product offerings.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.