Top Remote Data Engineer Jobs

Design and build maintainable data pipelines, ETL processes, and data architecture standards. Collaborate with product managers, analysts, and machine learning engineers to translate requirements into automated, scalable solutions. Apply software engineering best practices, asynchronous systems, APIs, streaming, testing, monitoring, and Python and SQL expertise. Help prioritize data initiatives based on business impact and communicate complex technical concepts to technical and non-technical stakeholders.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Build and maintain Databricks data pipelines, ETL/ELT processes, SQL transformations, and Power BI dashboards for HR and People Analytics. Ensure data quality, optimize models and reporting performance, troubleshoot issues, document solutions, and collaborate with HR stakeholders and technical teams to deliver scalable workforce analytics.

Designs, builds, and maintains scalable data platforms and resilient pipelines using Medallion architecture, Databricks, Apache Spark, Parquet, Iceberg, and Delta Lake. The role includes database optimization for PostgreSQL and SQLite, data quality and performance monitoring, ETL/ELT development, troubleshooting, documentation, and collaboration with analytics stakeholders.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Designs and operates secure batch and streaming data pipelines across cloud platforms. Responsibilities include data ingestion, integration, modeling, quality validation, governance, lineage, security, monitoring, workflow orchestration, and performance optimization. The role supports analytics, machine learning, AI-enabled applications, and authoritative data services while documenting architectures, runbooks, and data contracts. Collaboration with engineering, cloud, security, data science, architecture, and customer teams is required, along with contributions to secure data platform proposals.

Design and optimize Snowflake data warehouses, dbt transformation models, and scalable ETL/ELT pipelines. Develop ingestion utilities, improve SQL and warehouse performance, establish data quality and governance controls, and maintain CI/CD workflows using Git. Collaborate with analysts, data scientists, and business stakeholders across manufacturing, supply chain, and operations. The role also supports modern DataOps practices and may involve orchestration tools and cloud platforms.

Build and maintain scalable ETL/ELT pipelines using Azure Data Factory, Snowflake, dbt, and SQL. Develop data models, transformations, tests, and documentation; ingest data from APIs, databases, files, and enterprise applications; optimize performance; ensure data quality; troubleshoot failures; and support production deployments and monitoring.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Designs, builds, tests, maintains, and optimizes large-scale data pipelines, ETL processes, ingestion workflows, and cloud data storage. Develops Databricks data engineering and AI/ML pipelines, Azure Data Lake solutions, Power BI dashboards, and CI/CD automation. Ensures data quality, security, scalability, cost efficiency, and reusable engineering practices while collaborating with distributed technical teams.

Designs, builds, and maintains cloud data pipelines and ETL/ELT processes supporting VA healthcare analytics. Responsibilities include patient matching, record linkage, data harmonization, quality management, documentation, performance optimization, and pipeline support. Collaborates with analysts and stakeholders to develop scalable solutions and Power BI reports using Databricks SparkSQL. The role requires federal healthcare data experience, strong SQL or Spark SQL skills, and the ability to obtain a VA Public Trust clearance.

Lead the modernization of a healthcare data platform from an MSSQL XML object store to a scalable AWS architecture. Evaluate AWS storage and query services, design SQL reporting layers, architect data migration and metadata strategies, optimize performance and cost, and build CI/CD and infrastructure-as-code pipelines. Partner with business stakeholders, analysts, and C#/.NET engineers to deliver accessible data models and reliable integrations. Document architecture, data flows, and migration processes while supporting healthcare data and compliance requirements.

Supports the design and development of scalable end-to-end data pipelines and cloud data platforms. Responsibilities include improving data reliability and quality, implementing data management practices, supporting automation and process improvements, ensuring secure and cost-effective solutions, and recommending system enhancements. The intern collaborates with global teams and gains hands-on experience in data engineering, project coordination, and innovation management.

The Principal Data Engineer designs and builds scalable cloud-native data platforms, pipelines, frameworks, and governed data products supporting analytics, reporting, AI, and operational workloads. Responsibilities include Databricks, Spark, Delta Lake, Azure, SQL Server, batch and streaming processing, data integration, performance optimization, governance, modernization, troubleshooting, architecture reviews, and mentorship. The role provides senior technical leadership without direct people management and supports insurance-domain data across policy, claims, billing, finance, and regulatory functions.

Designs and maintains scalable data architectures, ETL workflows, batch and real-time pipelines, and SQL/NoSQL storage solutions supporting intelligence operations. Ensures data quality, governance, security, compliance, and performance across structured and unstructured intelligence datasets. Documents schemas and data lineage, collaborates with analysts and data scientists, and participates in technical and mission-planning forums.

Designs and maintains scalable data architectures, ETL workflows, batch and real-time pipelines, and SQL/NoSQL storage for intelligence operations. Ensures data quality, governance, security, compliance, documentation, and lineage across multi-INT datasets. Collaborates with analysts and technical teams to translate intelligence requirements into secure solutions and participates in mission planning and coordination forums.

Designs and maintains scalable data architectures, ETL workflows, batch and real-time pipelines, and SQL/NoSQL storage solutions supporting intelligence operations. Ensures data quality, governance, security, lineage, and compliance with IC and DoD standards. Collaborates with analysts and technical teams to deliver reliable intelligence data and participates in mission-planning and coordination forums.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

Design, implement, and optimize large-scale data ingestion, cleansing, transformation, loading, replication, and display systems. Develop data pipelines, profiling, deduplication, matching logic, and custom data-source solutions. Maintain data-layer documentation, collaborate with data scientists on AI/ML workflows, and use Java technologies, REST APIs, Airflow, ETL, SQL, and Linux in a Big Data environment.

The Forward Deployed Data Engineer will manage AI-automated data platform migration projects, ensuring timely delivery and communication with clients while utilizing proprietary tools.

Build and maintain trusted risk datasets, semantic models, dashboards, reports, and reusable analytics products. Translate stakeholder needs into analytics requirements, integrate fragmented risk and operational data, automate data preparation and reporting, establish data quality and governance standards, and produce insights on trends, control weaknesses, and emerging risks. The role also supports data product adoption, stakeholder enablement, and coaching without supervisory responsibilities.

Develops scalable data and software systems using Python and Apache Spark. Designs APIs, data models, ETL workflows, orchestration, and data interactions while optimizing performance, scalability, integrity, and security. Builds user interfaces, contributes to automated testing, applies Agile practices, and supports CI/CD delivery. The role requires strong software engineering, distributed computing, analytical, and communication skills, with AWS, R, Databricks, and SAS experience preferred.

Own Fullbay’s Snowflake data platform end to end, including dimensional modeling, certified metric definitions, semantic layers, identity resolution, transformations, governance, security, cost management, source ingestion, and AI-agent enablement. Build tested SQL and transformation code, establish company-wide data standards, document systems thoroughly, and direct an architecture consultant. Provide guidance for self-service data use while serving as the final authority on definitions, access, and platform design.