Top Remote Data Engineer Jobs

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

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.

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.

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.

Migrates customer data from legacy and competitor systems into SaaS products using SQL-based tools. Performs extraction, transformation, cleanup, validation, auditing, data mapping, and quality assurance. Develops repeatable scripts and workflows, collaborates with customers and cross-functional teams, supports testing and defect resolution, and documents migration processes. The role increasingly uses BigQuery, cloud platforms, automation, and intelligent tools while supporting global delivery across regions and time zones.

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.

Leads the design, development, and operation of scalable data platforms and ETL/ELT pipelines using Python, PySpark, Databricks, Spark, Delta Lake, and cloud services. Responsibilities include data modeling, performance optimization, governance, lineage, quality validation, security, compliance, CI/CD, monitoring, AI/ML enablement, and data product delivery. Partners with stakeholders and mentors engineers while supporting reliable enterprise healthcare data solutions.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Build and maintain ETL/ELT pipelines to move product data into analytics-ready stores (Postgres, data lake, warehouse). Design and optimize data models, ensure data quality and documentation, support ad-hoc research requests, and collaborate with research and engineering teams to enable reproducible analytics.

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.

Build and maintain streaming, replication, and data platform pipelines using Kafka, CDC, and Materialize. Manage schemas, data contracts, infrastructure as code, clusters, access controls, CI/CD, monitoring, and observability across cloud environments. Participate in on-call rotations, incident response, design reviews, and technical documentation. The role requires a bachelor’s degree, five or more years of data engineering or backend engineering experience, and authorization to work in the United States without sponsorship.

Build and maintain streaming, replication, and data platform pipelines using Kafka, CDC, and Materialize. Manage schemas, data contracts, infrastructure as code, clusters, access controls, CI/CD, monitoring, and observability. Participate in on-call support, incident resolution, design reviews, and technical documentation. The role requires expertise in data engineering, streaming systems, Python, SQL, and cloud infrastructure.

Build and maintain streaming, CDC, and replication pipelines using Kafka and Materialize. Manage schemas, data contracts, infrastructure as code, clusters, access controls, CI/CD, monitoring, and observability across shared cloud environments. Participate in on-call rotations, incident response, design reviews, and RFCs, while maintaining documentation and runbooks. The role requires strong Kafka, streaming systems, Python, SQL, and cloud infrastructure expertise.

Develop and optimize dbt data models for Snowflake and Materialize using medallion architecture. Author schemas and data contracts, configure clusters and role-based access, document datasets, resolve data issues, and partner with BI, analytics, and business teams to deliver reporting-ready data products.

Designs, builds, and maintains scalable batch and streaming data pipelines in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies warehousing best practices, and partners with business and IT teams to deliver reporting, analytics, and AI/ML data solutions.

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.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Contributes to building and optimizing a large-scale data lake and reliable data pipelines for cybersecurity analytics. Responsibilities include data modeling, distributed processing, workload optimization, monitoring, observability, CI/CD, code reviews, and supporting MLOps. The role collaborates with senior engineers to improve processes and deliver cost-effective cloud data solutions.

Develop scalable, maintainable, and high-performing data pipelines and enterprise data lake solutions. Partner with stakeholders and technology teams to create analytics solutions, reports, and dashboards that improve patient outcomes, operational efficiency, and data-driven decision-making. Support data quality and enterprise data initiatives across the organization.

Build, maintain, and optimize cloud data pipelines using SQL, Python, Snowflake, BigQuery, and dbt. Integrate data from APIs, databases, cloud storage, marketing platforms, and third-party sources. Implement data quality checks, monitoring, troubleshooting, automation, and performance improvements. Collaborate with architects, analytics teams, and stakeholders to deliver reliable, reusable, analytics-ready datasets.

Build and operate data pipelines ingesting and normalizing security data from multiple sources. Own entity resolution, asset attribution, data quality monitoring, automated correlation logic, and reporting support. Collaborate with security analysts and leadership to turn noisy data into actionable insights and improve remediation outcomes. Contribute to AI-augmented data analysis and correlation capabilities.

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.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Supports ETL pipeline implementation, data ingestion, data quality validation, schema and RBAC configuration, database performance testing, interoperability testing, and production migration activities. Maintains metadata registries, documents ingestion defects and remediation recommendations, validates post-migration data, and maintains database scripts and pipeline code in version control. The position is fully remote and supports a federal data migration program.