Top Remote Data Engineer Jobs

Build and operate scalable Databricks-on-AWS data pipelines using PySpark, Delta Lake, and lakehouse patterns. Optimize performance, implement data quality, monitoring, alerting, and automated remediation, and deliver curated datasets for BI and analytics partners. Collaborate with stakeholders on architecture and design while applying secure software engineering, CI/CD, agile, and operational stability practices. The role also uses AI-assisted development tools and supports workforce data analytics.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Designs, develops, deploys, and supports scalable cloud data engineering solutions for HEDIS processing and healthcare quality reporting. Builds ETL pipelines, data integrations, SQL transformations, and AI-enabled workflow automation. Collaborates with stakeholders, auditors, and product teams to ensure data quality, regulatory compliance, and operational reliability. Troubleshoots production issues, supports cloud modernization, applies CI/CD and testing practices, and mentors engineers as a senior individual contributor.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Leads enterprise data engineering strategy and architecture for pipelines, external data sharing, and scalable data platforms. Designs and codes critical data solutions while driving adoption of modern technologies, governance, resiliency, operational excellence, and unified data architecture. Advises stakeholders, mentors engineers, recruits talent, evaluates emerging data and AI technologies, and develops platform roadmaps. The role requires hands-on expertise across AWS, lakehouse architecture, streaming, distributed computing, data warehousing, and multiple programming languages.

Lead data modeling and ownership of critical pipelines supporting Block’s product data foundation. Build reliable, governed, and monitored datasets, data quality and lineage systems, experimentation infrastructure, and AI-assisted automation. Partner with product and engineering teams to translate business needs into end-to-end data solutions, participate in on-call support, and maintain pipeline SLAs.

Build and optimize data models, pipelines, monitoring, lineage, and data quality systems supporting Block’s product data foundation. Own critical data engineering solutions across their full lifecycle, ensure pipeline reliability through on-call support, and translate business and product needs into automated data solutions. Develop AI-assisted workflows and agents for data quality, issue resolution, and operational efficiency while partnering with engineering, product, data science, and machine learning teams.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Build and maintain scalable, AI-native data pipelines and dbt models using Snowflake, SQL, and Python. Optimize performance and costs, develop data quality and monitoring frameworks, support LLM-generated insights, and design enterprise-scale analytical architectures. Responsibilities include dimensional modeling, ETL/ELT automation, debugging, testing, secure coding, compliance, code reviews, and collaboration with data scientists and business stakeholders.

Build and maintain scalable batch and streaming data pipelines, governed risk data products, ETL/ELT workflows, and analytics-ready datasets. Integrate internal, vendor, and external data sources using cloud data lake and lakehouse technologies, including Microsoft Fabric and OneLake. Implement data quality, monitoring, lineage, reconciliation, CI/CD, testing, and operational support processes. Partner with analytics, risk, governance, security, and product teams to enable reporting, dashboards, advanced analytics, and AI capabilities.

Lead architecture, development, and optimization of a cloud-native data platform. Design high-volume batch and streaming pipelines, optimize data processing across AWS, Snowflake, Redshift, and Athena, and implement Iceberg-based data management. Establish security, privacy, observability, and DataOps practices while mentoring engineers, guiding technical direction, collaborating with data science teams, and delivering scalable petabyte-level data infrastructure.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Designs, builds, tests, and documents scalable data solutions and pipelines for CMS healthcare fraud, waste, and abuse initiatives. Responsibilities include data warehouse development, CI/CD deployment, orchestration, data quality, governance, AWS-based automation, testing, architecture support, and technical leadership. The role partners with analysts and business stakeholders, leads project initiatives, improves workflow automation, and ensures requirements and milestones are met.

Designs and maintains scalable data infrastructure for public health programs. Leads HL7 provider onboarding, configures Rhapsody message routing, supports interface testing and troubleshooting, builds data pipelines, and ensures secure data delivery to surveillance systems. Develops standardized workflows, documentation, and implementation playbooks while transferring knowledge to health department staff. Collaborates with technical and program partners to gather requirements, optimize systems, and support reliable data integration.

Develop and maintain master data management and integration capabilities, including data quality rules, governance processes, stewardship workflows, data validation, profiling, modeling, and integration design. Collaborate with enterprise stakeholders, data owners, stewards, and governance groups to define requirements, configure systems, and support strategic MDM initiatives. Use Python, SQL, AWS services, APIs, Dataiku, and ETL/MDM tools to improve data accuracy, consistency, transformation, and retention.

Builds and maintains scalable data pipelines, ETL/ELT infrastructure, analytics platforms, and data delivery frameworks. Responsibilities include data modeling, SQL optimization, quality assurance, governance, troubleshooting, cloud-based big data processing, and stakeholder support. The role also improves data infrastructure scalability, documents processes, educates users on data literacy, and provides guidance to junior staff. Requires a bachelor’s degree or equivalent experience and at least four years of relevant data engineering or analytics experience.

Develops enterprise data pipelines, streaming ingestion systems, AWS data lakes, Snowflake warehouses, analytics solutions, and MarkLogic integrations. Designs data and software architectures, REST APIs, and JavaScript modules while supporting Agile software development. Requires extensive experience with AWS data services, Python, JavaScript, Java, databases, ETL, web development, data integration, and enterprise information architecture.

Designs, builds, and maintains the ASPCA’s enterprise data warehouse ecosystem. The role develops dbt Core models across medallion architecture, integrates MDM workflows, builds and operates Microsoft Fabric and Azure Data Factory pipelines, and supports Snowflake environments. Responsibilities include dimensional modeling, orchestration, monitoring, testing, troubleshooting, performance optimization, documentation, CI/CD, data governance, and quality assurance. The engineer collaborates with BI, data management, strategy, and research teams to deliver trusted, analytics-ready datasets.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.