Top Remote Data Engineer Jobs

Lead end-to-end migration of document-heavy legacy systems to cloud, converting unstructured records into analytics-ready formats, ensuring data quality and governance. Build and maintain MuleSoft integrations and APIs, apply data transformation (DataWeave), support Salesforce Data 360 implementation, and ensure lineage, metadata, and compliance while collaborating in Agile teams for phased cutovers and audit readiness.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Designs, develops, and maintains Databricks ETL pipelines, data lake architectures, datasets, and data integrations supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains legacy systems, implements data quality controls, and supports data warehousing. Maintains SSRS reports and collaborates with business users, analysts, and IT teams to deliver reporting solutions. Documents work, communicates technical information, and contributes to AI-assisted development practices.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Designs and builds scalable, reliable, observable batch and streaming data pipelines and models. Partners with stakeholders on data integration, validates data quality, writes tests, participates in code reviews, documents solutions, troubleshoots production issues, and aligns technical work with business needs. The role requires strong programming, database, data modeling, governance, and Databricks knowledge, with preferred experience in Spark, AWS data services, CI/CD, testing, orchestration, and transformation frameworks.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Design and build a medallion architecture using dbt and Microsoft Fabric, create and maintain data ingestion pipelines, and implement governance, lineage, documentation, and quality checks to support self-serve analytics while collaborating with cross-functional teams.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Design and implement scalable data infrastructure in AWS using Spark and Scala. Evolve core data pipelines, store data in optimal engines/formats, build knowledge graphs and APIs, optimize AWS resources, implement automated data quality checks, and collaborate with Data Science and Product teams to deliver reliable, large-scale data services.

Designs and develops enterprise cloud data solutions, including batch, near-real-time, and API-based pipelines. Builds data models, warehouses, lakes, and lakehouses while implementing governance, quality, lineage, and curation practices. Supports CI/CD, testing, monitoring, observability, security, and compliance. Collaborates with architects, product owners, analysts, and leadership on platform strategy and modernization. Mentors engineers, leads reviews, presents technical topics, and explores AI-enabled data capabilities.

Build and support scalable big data pipelines, integrations, and Data Lakehouse architecture for marketing, analytics, reporting, and AI applications. The role develops data models, monitors data quality, troubleshoots production issues, provides post-deployment support, and partners with analytics, data science, business, and engineering teams on long-term platform strategy.

Build and own the data platform backbone: design ingestion patterns, Snowflake warehouses, governance, and freshness strategy. Implement reliable batch and streaming pipelines, data quality, observability, and onboarding processes. Lead legacy migrations, create data contracts and SLAs, and apply AI-native tooling to automate debugging, documentation, and operational workflows while partnering with Analytics, Engineering, and vendors.

Lead design and ownership of a modern data platform: ingestion, Snowflake governance, pipeline reliability, migrations, data contracts, observability, and AI-enabled tooling. Partner with Analytics, Engineering, and vendors to deliver trustworthy data products and operate production pipelines.

Design, implement, and maintain Azure-based ELT/ETL pipelines and data architecture. Migrate and normalize source data into ADLS, enforce quality controls, source control, documentation, and self-service analytics. Optimize storage/processing, audit pipelines, and explore AI-assisted tooling.

Design, develop, and maintain enterprise data solutions and pipelines using Azure data platform and Microsoft Fabric. Support data modeling, master data management, medallion architecture, data quality, and integrations to produce trusted, scalable data for analytics and BI.

Design and build Snowflake transformation layer and migrate legacy SQL to versioned dbt models. Define conformed dimensions/facts using Kimball principles, set modeling and CI/CD standards, implement data quality/lineage, support BI migration and AI/ML readiness, and mentor future hires. Hands-on work in dbt, SQL, and Python with stakeholder collaboration and governance for financial and operational data.

Own and optimize data infrastructure and ETL/ELT pipelines using SSIS and Microsoft Fabric. Build and maintain SQL Server data warehouse staging, transformations, and lakehouse ingestion. Implement CI/CD, version control with GitHub, monitor pipeline health, troubleshoot data issues, document data flows, and translate stakeholder requirements into data engineering solutions that support BI reporting.

The role involves architecting intelligence infrastructure, building data pipelines, collaborating with other teams, and ensuring data accessibility and security for threat detection systems.

The Data Engineer will design and maintain data pipelines, optimize cloud data warehouses, and collaborate with analytics teams while mentoring junior staff.

The AWS Cloud Data Engineer will design, develop, and enhance AWS data environments, build scalable Data Lake solutions, and lead proofs-of-concepts into production.

Analyze Power BI reports, datasets, semantic models, and Alteryx workflows to trace data from source systems through ETL processes. Identify databases, schemas, tables, views, and columns; understand SQL transformations; map data relationships; and create source-to-target mappings and data lineage documentation. Collaborate with stakeholders to validate reporting logic, document architecture, and identify redundant or obsolete data sources.

Develop and deploy scalable backend code, distributed systems, data pipelines, and reliable data infrastructure. Lead projects from design through production, improve platform scalability and performance, and collaborate with cross-functional engineering teams. The role requires strong programming, advanced SQL and data modeling, cloud platform experience, and infrastructure-as-code skills. Bonus experience includes large-scale processing frameworks and operating critical production systems with four-nines availability.

Design and maintain ingestion pipelines for 835/837 and scanned EOBs: OCR, LLM-assisted extraction, integrations (SFTP, clearinghouses), data modeling, validation, and reconciliation to produce deterministic, trustworthy healthcare records.