Top Remote Data Engineer Jobs

Designs, builds, and operates scalable batch and streaming data platforms using Spark, Python, SQL, Microsoft Fabric, and Lakehouse architectures. Develops ETL/ELT pipelines, dimensional models, data governance, observability, and CI/CD practices. Optimizes distributed workloads, supports AI and analytics use cases, leads technical design, and mentors engineers while partnering with architects, stakeholders, security, and governance teams.

Build and operate reliable data pipelines transforming flight test, simulation, and operational data into versioned, traceable ML training datasets. Responsibilities include ingestion, normalization, time alignment, validation, dataset lineage, anomaly mining, labeling workflows, deployment feedback loops, monitoring, and data coverage analysis. Collaborate with flight test and operations teams on instrumentation and data access. The role is on-site in Boston and requires production data engineering experience supporting machine learning pipelines.

Build and maintain Oracle EBS data replication and integration pipelines using GoldenGate and Qlik Replicate for Advana. Monitor performance, troubleshoot failures, optimize throughput and latency, document data flows, support BI dashboards, and ensure compliance with federal data governance and DoD cybersecurity requirements. Coordinate upgrades, patching, migrations, and collaboration with reporting, security, and IA teams.

Lead the design and operation of a scalable data platform, owning ingestion, orchestration, observability, lineage, data modeling, contracts, and ETL/ELT pipelines. Build Databricks and PostgreSQL solutions, implement data-quality controls, anomaly detection, governance, privacy, and compliance practices, and establish reusable engineering standards. Mentor engineers and ensure reliable, traceable data across markets and product lines.

Own and evolve Worldly’s production data lake, Postgres warehouse, CDC and streaming pipelines, dbt transformations, orchestration, BI access controls, and graph integrations. Maintain reliable, governed analytics infrastructure using Iceberg, Trino, Dagster, Terraform, and AWS. Support production generative AI workflows involving embeddings, similarity search, extraction, and classification. Responsibilities include data quality, monitoring, security, incident response, disaster recovery, schema governance, and collaboration with data science and analytics stakeholders.

Designs, administers, and optimizes data systems supporting FSA IAM applications. Develops migration plans from legacy AIMS and PAS systems, creates data models, mappings, layouts, dictionaries, and integration processes, and supports analytics and fraud detection. Manages database configuration, performance tuning, monitoring, backups, capacity, secure data handling, encryption, network data flows, token management, and documentation while collaborating with development, security, and operations teams.

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.

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.

Manage, design, and optimize data activities for federal IAM systems. Develop data models, file layouts, source-to-target mappings, data dictionaries, integrations, ETL processes, and analytics support for identity analysis and fraud detection. Administer and tune databases, including configuration, backups, monitoring, capacity planning, and performance optimization. Support secure handling of identity and PII data, network-related data flows, documentation, token management processes, and collaboration across development, security, and operations teams.

Architect and maintain NetBrain’s first cloud data warehouse, ELT pipelines, transformation layer, semantic models, and BI foundation. Own data quality, governance, lineage, access controls, and curated models supporting product usage, customer adoption, account health, and go-to-market reporting. Partner with stakeholders to establish reliable metrics and a standardized source of truth, while supporting AI-assisted analytics and natural-language reporting use cases.

Build and operate scalable data infrastructure and pipelines for video, imagery, telemetry, sensor, log, and mission data. Create searchable data lakes, curated and versioned ML datasets, labeling workflows, quality checks, lineage, monitoring, and reproducible dataset-generation processes. Partner with autonomy, perception, software, simulation, and field teams to support model training, evaluation, debugging, replay, and deployment workflows.

Designs, builds, and optimizes cloud data solutions using Microsoft Fabric and Azure. Develops data pipelines, Lakehouse architectures, ELT/ETL processes, data models, semantic models, and Power BI reporting. Partners with stakeholders to define requirements and technical designs, supports platform migrations, and implements data quality, governance, security, and monitoring practices. The role also contributes to AI and machine learning data initiatives and requires strong SQL, Python, PySpark, and modern cloud data engineering expertise.

Design, build, and own scalable data pipelines and warehousing architecture from ingestion through analytics and incentive payouts. Lead data platform architecture, dbt modeling, data quality, lineage, observability, performance, and cost management. Partner with engineers and analysts on telematics accuracy and driver incentive reporting. Use AI coding tools daily to develop and review pipelines, SQL, and data models.

Own and scale the company’s analytics infrastructure, including BigQuery data warehouses, dbt data marts, external data exchange, DataOps, data quality, governance, security, and cloud cost optimization. Partner with application database teams, analytics engineers, and data scientists to enable reliable metrics and scalable client data delivery.

Designs, builds, and operates scalable enterprise data platforms supporting ingestion, processing, storage, governance, analytics, and AI/ML. Responsibilities include developing batch and real-time pipelines, lakehouse and warehouse architectures, orchestration, infrastructure automation, CI/CD, monitoring, security, cost optimization, and observability. The role partners with data, ML, DevOps, security, and application teams, troubleshoots complex platform issues, establishes reusable engineering standards, contributes to architecture strategy, and mentors engineers.

Build and maintain scalable web crawlers to extract, validate, and structure product data from eCommerce sites. Use AI-driven techniques, browser automation, proxies, and APIs to handle dynamic content and anti-bot measures. Monitor performance, clean large datasets, collaborate with Product/Engineering/Data teams, and continuously improve crawling frameworks and automation.

Design, build, test, and maintain data architectures, database systems, and pipelines using GCP and BigQuery. Optimize data retrieval, automate processes, redesign infrastructure for scalability, collaborate with stakeholders, and deliver analytics tools that provide actionable business insights.

The role involves developing and maintaining the Enterprise Data Warehouse, optimizing queries, and supporting data analysis and business intelligence activities.

Design and build scalable cloud-native data pipelines and models to produce trusted, analysis-ready data. Ensure data quality, governance, and privacy controls, apply software engineering best practices, enable BI and AI/ML consumption, and collaborate with clients and cross-functional teams.

Lead the architecture and scaling of Overstory’s data platform for AI-powered vegetation analysis. Design and operate large-scale geospatial and temporal data pipelines, orchestration systems, event-driven workflows, service-oriented architecture, data quality frameworks, and observability practices. Drive architectural standards, system design, scalability, reliability, and traceability while mentoring engineers and collaborating with data, ML, and product teams.

Design, develop, and optimize scalable batch and real-time data pipelines on GCP. Implement Medallion Architecture, build Python and PySpark transformations, optimize BigQuery and SQL workloads, deploy Dataflow pipelines, and orchestrate workflows with Cloud Composer and Airflow. Manage GCS storage, CI/CD, version control, IAM, data governance, security, reliability, scalability, and cost efficiency across enterprise data platforms.

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 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.

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.

Build and maintain production-grade dbt data models and pipelines across ingestion, transformation, and visualization. Develop reliable datasets supporting reporting, analysis, finance, revenue recognition, and company decision-making. Collaborate with Finance, Engineering, Sales, Product, and Marketing to understand data needs, improve manual workflows, validate data accuracy, and create scalable solutions using modern data tools.