Top Remote Data Engineer Jobs

Design, build, and maintain scalable ETL/ELT pipelines using Python, Spark, Databricks, Airflow and SSIS. Integrate and cleanse diverse healthcare datasets, implement Unity Catalog for metadata and governance, optimize Spark performance and JVM tuning, support Medallion architecture, and collaborate with cross-functional teams to automate CI/CD, observability, and data quality processes.

As a Data Engineer, you will design and maintain data pipelines, build data storage systems, and support analytics workflows, ensuring data reliability and performance. You will collaborate with engineers to define data structures and improve data management practices.

You will design and optimize cloud-based data pipelines in AWS, develop automation frameworks, and enhance data architecture for financial reporting and actuarial modeling.

The Data Engineer will design and build scalable data systems, support AI/ML integration, and collaborate across teams for the development of Canoe's Alts Data Platform.

The Data Engineer will design and maintain ETL pipelines and data storage solutions, collaborating with cross-functional teams to ensure efficient data processing and delivery.

Lead engineering and automation for a data collaboration ecosystem: design secure, scalable Snowflake/Databricks clean room architectures, build ELT pipelines (Snowpark/PySpark), implement MLOps and AI/LLM integrations, enforce RBAC and privacy controls, drive observability, cost optimization, onboarding, and operational excellence for partner-facing data products.

As a Senior Data Engineer, you will enhance data products, build automated data pipelines, and improve data platform capabilities, collaborating with various teams to drive initiatives.

Design, build, and maintain scalable batch and streaming data pipelines using Python/PySpark on Databricks or Snowflake. Implement data models (OLTP/OLAP), develop advanced SQL, orchestrate workflows with Airflow, enforce governance via Microsoft Purview, build data quality frameworks, support cloud/DevOps practices, monitor production pipelines, and lead technical design and code reviews.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

Lead design and implementation of shared, reusable analytics data models and pipelines. Drive standardization, governance, observability, and CI/CD for analytics; partner with Data Science, Infrastructure, and Product to certify metrics, modernize orchestration, and integrate AI-native tooling.

Design and build enterprise data models, ETL/ELT pipelines and high-performance PySpark jobs on Azure/Databricks. Enable ML/AI with MLflow and model serving, secure restricted-data environments using Unity Catalog and access controls, deploy AI agents, and evaluate emerging data and AI trends.

As Lead Data Engineer, you will guide a team, manage the data platform, and oversee the development of ML infrastructure and feature stores.

Lead design, build, and optimization of a Snowflake-centric cloud data platform and dbt transformation layers. Develop reliable pipelines from sources like Salesforce, ensure data quality, performance tuning, and compliance with PHI/PII/HIPAA. Collaborate with analytics, product, and engineering teams to deliver scalable, documented, analytics-ready datasets and platform standards.

Build and operate scalable data pipelines and analytics-ready datasets using Python, dbt, and BigQuery. Develop Looker dashboards, ensure data quality and observability, optimize performance and cost, integrate data workflows with backend services and CI/CD, and support analytics, experimentation, and production use cases.

Design, build, and maintain dimensional data models in BigQuery using Dataform. Collaborate with stakeholders to deliver data products, document metadata and lineage, optimize performance and cost, ensure data quality and security, and support analytics/AI/BI initiatives.

Design, implement, optimize, and maintain scalable, high-availability database solutions for a SaaS healthcare product. Integrate client data, resolve complex data issues, refactor systems for Azure, optimize MSSQL performance, participate in code reviews, and deliver features within Agile/Scrum to ensure reliable, high-performance data services.

Design, build, and support scalable ETL/ELT data pipelines across IBM Netezza, DataStage and modern cloud platforms (Snowflake, Databricks). Develop complex SQL, troubleshoot production pipelines, and collaborate with BI and cloud teams for data ingestion, transformation, validation, and documentation.

Lead design and delivery of enterprise data centralization and automation for reporting and BI. Build and maintain ETL/SSIS pipelines, data orchestration, file delivery and quality controls, follow SDLC/agile, mentor junior engineers, and partner with business stakeholders to capture requirements and ensure timely data delivery.

Design, build, and deploy reliable, high-performance data pipelines for warehousing and real-time systems. Optimize ingestion, transformation, and storage; manage SLAs; implement data quality, observability, and governance; and collaborate with analysts, data scientists, and stakeholders globally.

Lead modernization of core data systems and build scalable, secure data pipelines for cloud and on‑prem environments. Design data contracts, observability, lineage, and real‑time processing for vulnerability management. Partner with product and leadership to align data strategy and deliver customer-facing data solutions.

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.

Develop and optimize data pipelines for reporting and advanced analytics. Ensure data quality and create real-time processing applications. Collaborate with DevSecOps teams and enhance data management procedures.

Build and maintain cloud data pipelines and ML feature/model workflows in Snowflake and AWS. Implement data ingestion, transformations, quality checks, monitoring, and documentation. Collaborate with data scientists, analysts, and product teams to support analytics, audience insights, rapid prototyping, and reliable production deployments.

Design, build, and maintain ELT pipelines and analytics-ready data models to support reporting, KPIs, and downstream ML use cases; implement data quality, optimize pipeline performance, document lineage, and ensure governance and privacy.

Design and maintain ETL pipelines and graph data models to ingest security telemetry into an AWS Neptune knowledge graph, enable ML-driven threat detection, optimize query performance, ensure data quality and compliance, and document workflows.