Top Remote Data Engineer Jobs

Maintain and monitor Snowflake data warehouse and ingestion pipelines. Troubleshoot file/load/data quality issues, operate SFTP/drop-zone/file automation, support scheduled loads, build dbt models and tables, document runbooks, and serve as backup for core warehouse operations while collaborating with engineering and IT.

Design and build data warehouse, lakehouse, and analytics solutions on Microsoft Fabric and Azure SQL. Implement Medallion Architecture, develop reliable ELT/ETL pipelines, model data for analytics, ensure data quality and monitoring, consult with clients on requirements and governance, and provide technical leadership and mentoring.

Build and operate low-latency, real-time data and ETL pipelines for sports analytics products. Support production systems during live events, develop predictive analytics and APIs, and integrate complex real-time datasets into consumer and enterprise offerings.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Design, develop, and maintain enterprise data integration (ETL) systems and data platforms. Lead technical designs, optimize performance and scalability, support production issues and off-hours rotations, build reporting and BI solutions, document data processes, and collaborate with stakeholders and vendors to align business requirements.

Design, develop, and optimize ETL/data integration, data platforms, and BI solutions. Lead technical designs, troubleshoot production issues, participate in on-call support, and document processes to ensure scalable, secure, high-performance enterprise data solutions.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Design, build, and maintain scalable ETL/ELT pipelines and Lakehouse solutions; ingest and transform diverse data formats; optimize Databricks and SQL Server environments; implement data quality, lineage, and governance; support batch and streaming ingestion; collaborate with analysts and stakeholders to enable analytics (fraud/anomaly detection, financial oversight); troubleshoot and improve pipelines. Candidates must be willing to undergo a U.S. government background investigation.

Design, build, and operate end-to-end production data pipelines on GCP/BigQuery at scale. Optimize partitioning, clustering, query performance, and cost/slot management. Integrate and maintain ETL/ELT processes, legacy SQL Server reporting (stored procedures, SSRS), data validation/monitoring, and data modeling. Use intermediate Python for automation and work with BI tools (Power BI, Looker, Tableau). Champion AI tool adoption and mentor teammates while ensuring delivery and client alignment.

Senior Exasol data engineer to design, build, optimize, and validate SQL-based data pipelines for healthcare analytics. Responsibilities include complex query tuning, extracting/staging from SQL Server, chart validation, applying clinical terminologies, quality measure development, and maintaining disciplined version-controlled engineering practices.

Design, build, and maintain scalable ETL/ELT pipelines and data models using Databricks, Spark, dbt, SQL, and AWS services. Optimize ingestion, transformation, orchestration, and monitoring across structured and semi-structured data. Collaborate in Agile teams, support analytics and reporting for large-scale federal environments, and evaluate new data tools and platform improvements.

Design, build, and maintain scalable AWS-native data platforms and ETL/ELT pipelines using Databricks, Spark, Python, dbt, and SQL. Implement ingestion, transformation, orchestration, and analytics solutions (Iceberg, Parquet, Redshift, Athena), support FedRAMP/NIST controls, migrate legacy systems to cloud-native services, and mentor junior engineers while collaborating in Agile teams.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Design, build, and maintain end-to-end batch and streaming data pipelines; develop ETL/ELT workflows and modular Python code; write and optimize SQL; implement analytics data models; monitor data quality, troubleshoot failures, and document pipelines; collaborate with stakeholders and mentor teammates. Remote role for candidates based in Latin America.

Design, build, and manage batch and real-time data pipelines on GCP. Implement Terraform IAC, CI/CD, APIs, and CDC ingestion via Datastream. Build medallion architecture with Dataform/dbt, optimize BigQuery and ClickHouse models, create Airflow DAGs with monitoring, and maintain GDPR/CCPA compliance and data governance.

The Data Engineer will develop and maintain data pipelines, build data models, ensure data quality, and support cross-functional teams while improving internal data platform infrastructure and documentation.

Design, build, and maintain scalable ETL/ELT data pipelines on Databricks and AWS S3; manage large geospatial and temporal datasets; productionize ML models; implement data validation, testing, and monitoring; optimize storage and processing; document workflows; collaborate cross-functionally.

The DMS Data Engineer is responsible for analyzing raw data, building data systems and pipelines, conducting complex data analyses, and collaborating on projects to enhance data quality and reliability.

Design, build, and maintain scalable cloud data pipelines and ETL/ELT processes using Azure Data Factory, Snowflake, SQL, and Python. Integrate enterprise and third-party data, validate and model datasets for BI, reporting, automation, and AI, monitor and optimize pipeline performance, and document data flows while partnering with stakeholders.

The Staff Data Engineer will lead design and implementation of analytics platforms and data products, collaborate cross-functionally, mentor engineers, and drive best practices at Upside.

We are seeking a Data Engineer to design, build and optimize scalable data pipelines. Strong experience in Databricks, PySpark, and data warehouse concepts is required.