Top Remote Data Engineer Jobs

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead architectural decisions, evaluate platforms such as Snowflake and Databricks, establish engineering patterns, and deliver resilient data solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical outcomes to stakeholders. Drive large-scale data initiatives independently in an Agile environment.

Data Engineering Interns will define metrics, instrument logging, ingest and model data, transform datasets, ensure data quality and governance, and support visualization, reporting, alerting, and scalable systems. They will partner with product, analytics, and data consumers to improve product engagement, efficiency, costs, and strategic decision-making. The 12-week, full-time internship includes technical training, mentorship, and hands-on project experience.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

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.

Leads data engineering within an agile team, designing secure and scalable data platforms, pipelines, architectures, storage, access, analytics, governance, backup, and recovery strategies. Develops advanced data models, evaluates security controls, documents best practices, and drives responsible AI-assisted engineering adoption. Provides technical leadership through architecture decisions, mentoring, cross-team influence, and operational support using Python, Databricks, Spark, AWS, and modern lakehouse patterns.

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

Build and own scalable data platforms and ingestion pipelines using BigQuery, dbt, and modern ingestion tools. Design batch and CDC workflows, improve reliability and observability, implement governance and access controls, and support analytics and AI agents. Develop Python services and internal tooling, contribute to architecture and lakehouse experiments, collaborate with stakeholders, and mentor engineers. The role requires strong SQL, Python, cloud infrastructure, testing, CI/CD, and AI-assisted development experience.

Lead design and build of ETL pipelines and Gold Layer tables in Databricks; develop semantic layers, data dictionaries, and AI-ready documentation; create dashboards and ad-hoc analyses to surface marketing and sales KPIs; partner with stakeholders to translate requirements into analytics-ready datasets and enable self-serve BI and agentic AI workflows.

Designs and leads Azure data engineering solutions for a healthcare data warehouse modernization project. Responsibilities include building ETL pipelines for JSON and HL7 FHIR data, developing PySpark transformations, modeling enterprise clinical data, implementing monitoring and alerting, and supporting large-scale database migrations. The role requires collaboration on cloud architecture, Infrastructure as Code, REST APIs, CI/CD, and DevOps practices, while providing technical leadership for Azure data solutions.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

Designs, builds, and supports scalable data pipelines, cloud-native processing systems, data models, APIs, and analytics solutions. The role ensures data quality, observability, testing, governance, CI/CD, and production reliability while partnering with engineering, product, analytics, BI, and business stakeholders. Responsibilities include architecture decisions, troubleshooting, documentation, Agile delivery, incident response, and mentoring engineers.

Designs scalable data architectures, ELT/ETL pipelines, data models, quality frameworks, and self-service data platforms. Partners with engineering, product, program, leadership, and data science teams to translate business needs into technical solutions. The role includes end-to-end technical ownership, large-scale batch and streaming data processing, mentoring junior engineers, system optimization, and on-call support for platform stability.

Lead the architecture and deployment of scalable data platforms, real-time and batch pipelines, AI infrastructure data systems, APIs, data lakes, warehouses, streaming architectures, and data quality frameworks. Optimize distributed processing and queries, establish governance, model enterprise data semantics, evaluate modern data technologies, and mentor engineering teams while collaborating across analytics, product, and platform groups.

Designs and maintains production data pipelines, models claims and operational data, and improves reliability through testing, monitoring, and alerting. Owns workflow troubleshooting, backfills, performance optimization, documentation, access controls, and CI/CD practices. Partners with business and technical teams to define requirements and deliver trusted datasets using SQL, Python, cloud data warehouses, and orchestration tools.

Design and implement scalable data infrastructure, pipelines, integrations, and models. Build automated insurance reporting pipelines, unify siloed datasets, enable self-service analytics, and enforce governance, quality, security, testing, and reliability standards. Partner with Risk, analytics, product, and cross-functional teams to deliver data solutions using modern data engineering tools and practices.

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, build, and optimize scalable data pipelines, warehouses, lakes, and processing systems for analytics and business intelligence. Ensure data quality, security, reliability, and cost efficiency while collaborating with data scientists, analysts, and engineers. Maintain technical documentation, support Databricks solutions, and apply emerging data engineering technologies. The role requires strong programming, SQL, ETL, orchestration, data modeling, cloud, and big data expertise.

Architects and leads enterprise data engineering platforms, including scalable ETL/ELT pipelines, streaming frameworks, cloud infrastructure, data warehouses, and AI/ML data systems. Establishes engineering standards, governance, monitoring, data quality, security, and schema management practices. Designs AI agents using LLMs, partners across business and infrastructure teams, and makes high-impact architectural decisions for reliable, secure, multi-region data environments.

Builds and maintains cloud data pipelines, transformations, semantic views, conformed dimensions, and analytical data models. Implements data quality controls, lineage, documentation, testing, release-readiness evidence, and defect resolution. Collaborates with data owners, governance teams, engineers, security stakeholders, data scientists, and customers to deliver reliable, governed data products supporting analytics, reporting, self-service BI, and AI/ML use cases.

Build and maintain Ab Initio data pipelines for enterprise data lakes and warehouses. Perform testing, debugging, data profiling, quality-rule definition, business-term mapping, and data analysis for ingestion and curation. Document data lineage in enterprise metadata repositories and collaborate with product, program, Scrum, data, and business teams in a SAFe Agile environment. The role requires SQL, shell scripting, AWS, database, CI/CD, and scheduling-tool knowledge.

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.

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.

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.