Top Remote Data Engineering Jobs

The Senior Data Engineer will focus on Data Modeling, utilizing SQL, Snowflake, and Apache Airflow to develop efficient data models and deliver data products.

Design, build, and maintain production data pipelines and tooling (CI/CD, testing, validation) using Snowflake, dbt, and orchestration tools. Model, transform, document, and optimize data to produce reliable curated datasets for analytics and solutions, enforce engineering best practices, and mentor teammates while ensuring scalability, observability, and data integrity.

The Analytics Manager will build and maintain data pipelines, develop statistical models, create self-serve analytics tools, and partner with teams to measure impact and identify opportunities within blockchain and Web3.

Validate and monitor supplier and customer data quality, automate checks with SQL and Python, build dashboards (Tableau), support customer onboarding/deliveries via AWS and Snowflake, provide analytics and direct customer support, and improve data delivery processes.

Lead design, build, and support of cloud-based ETL, data warehousing, and reporting solutions. Develop/optimize ETL pipelines, data models, and queries; deploy containerized services on Kubernetes; automate workflows; mentor teammates; collaborate with architects, data scientists, and stakeholders to deliver scalable analytics and reporting products.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

Architect, build, and maintain scalable ELT data pipelines and analytics-ready data marts. Lead dbt modeling, ensure data quality, CI/CD, and documentation. Partner with stakeholders, mentor junior engineers, evaluate tooling (reverse ETL), and drive long-term data platform strategy.

Lead data platform strategy and architecture, scale Spark-based ETL/ELT pipelines, improve reliability and cost efficiency in an AWS environment, strengthen governance and lineage, modernize ingestion (Fivetran), and mentor engineers while delivering production-grade data solutions for analytics and AI.

Manage and mentor a team of data scientists and engineers, guide development and delivery of ML/AI solutions, ensure best practices, collaborate with stakeholders, and align data science work with business objectives.

Design, build, and maintain production data pipelines and the unified data model using Snowflake, dbt, and orchestration tools. Optimize, test, document, and monitor ELT workflows, enforce engineering best practices, mentor teammates, and deliver trustworthy data products for analytics and business decisions.