This page tracks remote data engineer openings that are location-eligible for Nebraska.
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Work for a Fortune 500 company that rewards performance, invests in your growth, and provides a launchpad for a high-earning remote sales career. This isn’t just a job — it’s your path to leadership, income, and long-term success.
Role Description We are actively hiring and scheduling interviews this week for a fully remote Work From Home position. Immediate hiring – secure your spot and get hired. Entry level position for the applicants and full training provided. This is a legitimate opportunity with full training provided and guidance to obtain your Life & Health Insurance license. No prior experience required. We are looking for motivated U.S. residents ready to grow in a long-term remote career. - Communicate professionally with clients - Provide information and guidance - Follow a structured system - Maintain consistent performance Qualifications - Strong communication skills - Reliable internet connection - Self-motivated and coachable - Must be a U.S. resident - Willingness to obtain a Life & Health Insurance license (assistance provided) Benefits - 100% Remote - Full training program - Licensing guidance and support - Advancement opportunities - Supportive leadership team Company Description
Role Description Our client is looking for an AI / Data Engineer to design and deliver data platforms, pipelines and AI-enabled solutions for our clients. This is a hands-on consulting and delivery role for someone who can take ownership of ambiguous client problems, shape a practical technical approach, and deliver robust solutions across data engineering, data architecture and AI innovation. You will work with the company’s colleagues, client stakeholders and external technical teams to acquire, structure, transform and expose data through analytics, applications, APIs and AI-enabled experiences. Duties & Responsibilities - Own the delivery of client data and AI engineering work from discovery through design, implementation, testing and deployment. - Work with client stakeholders to understand business problems, clarify requirements and translate them into practical technical solutions. - Design and build data ingestion pipelines from APIs, third-party systems, files, online sources and operational platforms. - Develop scalable warehouse and transformation layers that convert raw data into trusted, reusable client data products. - Apply sound data architecture, modelling, quality, lineage and governance practices. - Identify and implement opportunities to use AI, automation, retrieval and agent-based workflows within client solutions. - Build or support APIs, data access layers and application integrations that make data products usable by reporting tools, software applications and AI experiences. - Contribute reusable technical patterns, documentation and engineering standards across the company. Qualifications - Ownership and Client Problem-Solving: You take ownership of outcomes rather than waiting for fully defined requirements. You are comfortable working through ambiguity, asking the right questions, identifying gaps and helping clients move from a business problem to a practical solution. - AI-First Mindset: You are interested in how AI can improve data collection, enrichment, research, automation, retrieval and decision support. You do not need to be an AI researcher, but you should be comfortable evaluating where LLMs, agents and AI-enabled workflows can create practical value. - Data Engineering and Warehousing Capability: You have strong hands-on experience designing and delivering modern data pipelines and warehouse solutions. You understand ingestion patterns, orchestration, data modelling, transformation layers, quality controls, observability, lineage, performance and security. - Collaborative Delivery Mindset: You work well in a consulting environment, communicate clearly with technical and non-technical stakeholders, and are comfortable sharing ideas, challenging assumptions and working closely with client and internal delivery teams. Requirements - Approximately five to ten years of relevant experience in data engineering, analytics engineering, data platform delivery or related technical consulting roles. - Practical experience with several of the following: - Modern cloud data warehouses such as Snowflake, BigQuery, Redshift, Databricks or Synapse. - SQL, data modelling and transformation frameworks such as dbt. - Cloud platforms, particularly AWS, Azure or GCP. - API integration, external data ingestion and pipeline orchestration. - Data quality, monitoring, observability and governance practices. - Software engineering practices including Git, automated testing and CI/CD. - Building data products for analytics, reporting, APIs or software applications. - Experience with AI orchestration frameworks, LLM tool calling, retrieval workflows, vector search, agent-based systems or related AI technologies. Success in This Role Success will mean delivering reliable, scalable and commercially useful data and AI solutions for the clients. You will combine sound engineering discipline with strong client problem-solving: understanding the real need, designing the right solution, delivering it effectively and helping clients derive measurable value from their data. Kindly regard your application as unsuccessful if you have not heard from the agency within 2 weeks.
Peraton Corporation, a national security company headquartered in Herndon, Virginia, supplies solutions for mission-critical programs and systems. Founded in 20
Role Description We are looking for a Data Scientist / ML Platform Engineer to contribute across the full ML development lifecycle — from model building and experimentation to production deployment and monitoring. Core responsibilities are in applied data science and MLOps, with secondary contributions to data engineering and light platform operations. This role works within established platform patterns alongside dedicated infrastructure engineers, without requiring their involvement for routine ML and data tasks. All work is performed in a HIPAA-governed, FedRAMP-compliant healthcare analytics environment. - Develop, train, and evaluate ML models (classification, regression, clustering, anomaly detection) and contribute to LLM-based capabilities such as RAG pipelines and prompt evaluation. - Support model governance and deployment practices using MLFlow, including experiment tracking, model versioning, registry promotion workflows, and automated testing across the ML lifecycle. - Contribute to production ML operations: model performance monitoring, drift detection, automated alerting, and incident escalation to maintain reliability and SLA compliance. - Build and improve model serving infrastructure, feature pipelines, and lifecycle automation to support reproducible, scalable model development and inference. - Apply explainability techniques (e.g., SHAP, LIME) and produce technical documentation to support stakeholder transparency and compliance requirements. - Contribute to data ingestion, ELT/ETL transformation, and pipeline reliability using Spark and SQL-based frameworks within Snowflake and Databricks environments. - Support pipeline orchestration, medallion architecture conventions, and data stewardship practices (metadata management, PII handling, lineage tracking in Unity Catalog). - Perform occasional system administration tasks in collaboration with platform teams, including environment configuration, access management, compute troubleshooting, and secrets handling using platform-native tools. Qualifications - Associate's with 6 years, or Bachelor's degree with 4+ years of relevant experience, or Master's degree with 2+ years of relevant experience or High School diploma with 8 years of experience in lieu of a degree. - Demonstrated experience with SQL and Python, including Python-based ML frameworks (e.g., scikit-learn, XGBoost, PyTorch, or TensorFlow). - Hands-on experience with MLFlow or equivalent tools for experiment tracking, model governance, and lifecycle management. - Strong understanding of SDLC fundamentals and experience with GitHub or equivalent version control. - Experience with distributed compute environments (e.g., Spark, Databricks) and cloud-native services. - Basic proficiency with Bash or shell scripting for automation and environment setup. - Ability to collaborate across multidisciplinary teams and communicate technical concepts to varied audiences. - Ability to obtain and maintain a Public Trust clearance. - US citizenship required or Green Card holder and must have been in the USA for 3 of the last 5 years. Requirements - Experience with MLOps practices including CI/CD for ML, containerization, feature pipeline automation, and model deployment frameworks. - Experience with Databricks E2 components (Unity Catalog, Feature Store, Delta Live Tables) and/or model serving and drift monitoring tools (e.g., Databricks Model Serving, Evidenly, etc.). - Experience with LLM frameworks (e.g., LangChain, LlamaIndex, Hugging Face Transformers) and familiarity with model explainability libraries (e.g., SHAP, LIME). - Advanced Spark performance optimization experience and/or API development using Databricks REST APIs. - Experience with healthcare analytics data (preferably Medicare or Medicaid) and familiarity with HIPAA or FedRAMP compliance constraints. - Experience building data pipelines in a Snowflake or Databricks environment. - Familiarity with orchestration tools (Airflow, Databricks Workflows). - Exposure to streaming data patterns using Spark Structured Streaming, Delta Live Tables, or Kafka. - Familiarity with environment reproducibility tooling (Docker, conda) and scripting (Python, Bash) to support automation and CI/CD tasks. Benefits - Target Salary Range: $80,000 - $128,000. This represents the typical salary range for this position. - Salary is determined by various factors, including but not limited to, the scope and responsibilities of the position, the individual’s experience, education, knowledge, skills, and competencies, as well as geographic location and business and contract considerations. - Depending on the position, employees may be eligible for overtime, shift differential, and a discretionary bonus in addition to base pay. Company Description Peraton is a next-generation national security company that drives missions of consequence spanning the globe and extending to the farthest reaches of the galaxy. As the world’s leading mission capability integrator and transformative enterprise IT provider, we deliver trusted, highly differentiated solutions and technologies to protect our nation and allies. Peraton operates at the critical nexus between traditional and nontraditional threats across all domains: land, sea, space, air, and cyberspace. The company serves as a valued partner to essential government agencies and supports every branch of the U.S. armed forces. Each day, our employees do the can’t be done by solving the most daunting challenges facing our customers.
• Implicarse y liderar conversaciones, modelos y decisiones que estructuran los datos de una empresa global, transformando procesos de negocio complejos en modelos de información claros, reutilizables y alineados con la estrategia de datos. • Mantener y evolucionar el Enterprise Data Model y el Data Domain Map de la organización. • Definir conceptos, taxonomías y estándares semánticos que todos puedan entender y aplicar. • Liderar reuniones con stakeholders de negocio para entender cómo funciona el negocio y modelarlo en estructuras de información consistentes. • Colaborar con equipos de datos, arquitectura de soluciones e ingeniería para asegurar la correcta implementación de los modelos en entornos basados en Google Cloud y herramientas de analítica como Power BI. • Definir e impulsar buenas prácticas de modelado en toda la organización, fomentando la adopción de modelos compartidos entre diferentes dominios de negocio. • Garantizar la alineación entre arquitectura de información, gobierno del dato y calidad de datos. • Participar en la mejora continua de herramientas como catálogos de datos y sistemas de gestión de metadatos.
We are a Y-Combinator-backed startup building your AI-powered Recruiter Agent
Role Description We are seeking an Engineering Subject Matter Expert to help develop next-generation AI systems by applying real-world engineering expertise to evaluate, create, and improve technical content. In this role, you will review engineering documentation, assess technical accuracy, and contribute practical engineering knowledge that enhances AI reasoning and performance. No prior AI experience is required—your engineering expertise is what matters most. Key Responsibilities - Technical Review & Analysis - Review and analyze engineering documentation, including technical reports, design documents, specifications, and project analyses. - Evaluate engineering submissions for technical accuracy, completeness, clarity, and adherence to industry best practices. - Provide detailed technical feedback, clarifications, and expert recommendations on complex engineering scenarios. - Content Development - Develop real-world engineering examples, case studies, and problem sets across various engineering disciplines. - Create structured technical explanations to help improve AI understanding of practical engineering concepts. - Ensure all technical content is accurate, well-organized, and aligned with engineering principles. - AI Model Evaluation - Assess AI-generated engineering responses for correctness, logical reasoning, and technical rigor. - Identify errors, inconsistencies, or gaps in engineering outputs and provide actionable feedback to improve model performance. - Contribute to the development of high-quality engineering datasets for AI training and evaluation. - Collaboration & Process Improvement - Work closely with project teams to ensure deliverables align with project milestones and quality standards. - Share industry best practices and technical insights to enhance project methodologies. - Collaborate effectively in a remote environment while maintaining high-quality documentation and deliverables. Qualifications - Bachelor's, Master's, or PhD in Mechanical, Civil, Electrical, Chemical, Industrial, Aerospace, Electronics, Manufacturing, or any other Engineering discipline. - Experience preparing technical reports, engineering documentation, design specifications, or project analyses. - Strong analytical and problem-solving skills with attention to technical detail. - Excellent written and verbal communication skills with the ability to explain complex engineering concepts clearly. - Ability to work independently and manage deliverables in a remote work environment. Preferred Qualifications - Experience working on multidisciplinary or cross-functional engineering projects. - Ability to communicate technical concepts effectively to both technical and non-technical audiences. - Familiarity with engineering standards, regulatory compliance, quality assurance, or industry best practices. - Experience reviewing engineering documentation or providing technical guidance. - Passion for innovation, technology, and engineering education. Must-Have Skills - Engineering Documentation - Technical Analysis - Technical Report Writing - Problem Solving - Engineering Design Review Good-to-Have Skills - Quality Assurance - Regulatory Compliance - Technical Documentation - Cross-functional Collaboration - Engineering Standards - Project Analysis - Design Documentation - Research & Technical Writing
• Design the ETL, transformation, and modeling patterns the team builds on • Build and maintain data ingestion pipelines that move data reliably from source into the warehouse • Build and maintain transformation models — client-specific and shared • Own data quality monitoring end-to-end: define what we monitor and to what SLA — not just tune thresholds — and decide where to spend the coverage budget • Understand the full data flow from raw event ingestion through final reporting tables • Own the complex, ambiguous requests and build the self-serve tooling that keeps the routine queue off engineering's plate
• Legacy Data Platform Support • Maintain and enhance SSIS packages for data extraction, transformation, and loading • Support SQL Server data warehouse (staging, ODS, reporting layers) • Troubleshoot data issues, job failures, and performance bottlenecks • Optimize SQL queries, stored procedures, and indexing strategies • Ensure reliability of scheduled jobs via SQL Server Agent • Cloud Data Engineering (Azure + Databricks) • Design and develop data pipelines using Azure Data Factory (ADF) • Ingest and organize data into Azure Data Lake (Bronze/Silver/Gold layers) • Build scalable data transformations using Databricks (Spark SQL, PySpark) • Create curated, analytics-ready datasets for Power BI • Implement Delta Lake and support data governance (e.g., Unity Catalog) • Analyze and document existing SSIS/SQL pipelines • Translate legacy ETL processes into modern ELT patterns • Support phased migration strategy (coexistence of legacy and modern platforms) • Reduce technical debt and improve pipeline maintainability • Establish standards for data modeling, naming, and architecture • Design dimensional models (fact and dimension tables) aligned to business processes • Integrate and standardize data across multiple ERP systems • Translate business requirements into scalable data solutions • Partner with stakeholders to identify high-impact use cases for data and analytics • Deliver datasets that enable reporting, forecasting, and operational insights • Implement data validation, reconciliation, and monitoring processes • Ensure data accuracy and consistency across systems during migration • Define and enforce data quality standards and controls • Support data lineage, documentation, and transparency initiatives • Work closely with business stakeholders, analysts, and BI developers • Support Power BI semantic models and reporting solutions • Communicate technical solutions in business terms • Act as a bridge between IT/data teams and business functions
NAVTECH INC 1600 Golf Road. Suite 1200, Rolling Meadows, IL 60008 Ph: (224) 348-1340 Email: alex@navtechusa.com Website: www.navtechusa.com E-Verified Company
Role Description Designs and develops scalable solutions using AI tools and machine-learning models. - Performs research and testing to develop machine learning algorithms and predictive models. - Utilizes big data computation and storage tools to create prototypes and datasets. - Conducts model training and evaluation. - Integrates, tests, tunes, and monitors solutions. - Proficient with multiple AI tools such as Python, Java, or R and machine learning frameworks like Spark, TensorFlow, or scikit-learn. Requires a master's degree in computer science, mathematics, engineering or equivalent. Typically reports to a manager or head of a unit/department. - P05-Expert: Works autonomously. Goals are generally communicated in "solution" or project goal terms. - May provide a leadership role for the work group through knowledge in the area of specialization. - Works on advanced, complex technical projects or business issues requiring state of the art technical or industry knowledge. - Typically requires 10+ years of related experience. Qualifications - Master's degree in computer science, mathematics, engineering or equivalent. - 10+ years of related experience. Requirements - Proficient with AI tools such as Python, Java, or R. - Experience with machine learning frameworks like Spark, TensorFlow, or scikit-learn. Company Description
We connect top talent with vetted employers, competitive pay, and real growth opportunities. Please NOTE: It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps—the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.
Role Description We are seeking a Software Engineer to help build and scale a graph-database-backed social listening platform. This role sits at the intersection of backend data infrastructure and front-end visualization, supporting a product that ingests high-volume social and web data in real time and visualizes complex network relationships. The ideal candidate is comfortable working across graph databases, streaming data pipelines, and modern front-end frameworks, and thrives in a fast-moving, early-stage engineering environment. - Design, build, and maintain backend services that interface with graph databases to model and query complex relationship data - Develop and maintain real-time data ingestion pipelines handling high-throughput social and web data - Build and optimize web scraping and crawler infrastructure to support continuous data collection - Develop front-end features and dashboards that visualize network and relationship data in an intuitive, performant way - Collaborate cross-functionally to translate product requirements into scalable technical solutions - Write clean, maintainable, well-tested code across the stack - Troubleshoot performance bottlenecks across data ingestion, storage, and visualization layers Qualifications - Hands-on experience with graph databases (such as Neo4j, TigerGraph, Dgraph, ArangoDB, or Amazon Neptune) and graph query languages (such as Cypher or Gremlin) - Experience with real-time/streaming data infrastructure (such as Kafka, Kinesis, Flink, or Spark) - Strong proficiency in at least one backend language such as Python, Go, or Node.js/TypeScript - Front-end development experience with React, Next.js, and TypeScript - Experience building or working with data pipelines, ETL processes, or web scraping/crawler infrastructure - Comfortable working in a high-growth, early-stage engineering environment with evolving priorities - Must be authorized to work in the United States without sponsorship Preferred Qualifications - Experience with data visualization libraries such as D3, Cytoscape, Sigma.js, vis.js, or Recharts, particularly for graph/network visualizations - Experience with social listening, social media data, or large-scale web data platforms - Familiarity with Elasticsearch, OpenSearch, Redis, or Airflow - Prior experience at a seed-to-Series B startup - Experience with AWS, GCP, Docker, or Kubernetes Tools & Technology - Graph database platforms (Neo4j, TigerGraph, Dgraph, ArangoDB, or similar) - Streaming/data pipeline tools (Kafka, Spark, Flink, or similar) - React, Next.js, TypeScript - Python, Go, or Node.js - Cloud infrastructure (AWS/GCP), Docker, Kubernetes
At CIGen, we partner with both startups and established enterprises to help them achieve their business goals through software solutions. We are a Microsoft Gold Partner. Our company is driven by core values such as professionalism, trust, and mutual respect. We believe that the only way to achieve long-term business success is by building long-term, trusted relationships with clients, contractors, and partners. Therefore, the quality of our services is crucial!
Role Description We are looking for a .NET Data Engineer to join our team and work on building scalable data platforms and cloud-native backend services. This role is ideal for an engineer with a strong .NET background who has experience (or growing expertise) in data engineering, cloud platforms, and modern analytics ecosystems. You will work at the intersection of backend development and data engineering, designing data pipelines, integrating cloud services, and enabling data-driven and AI-powered solutions. 🌍 This position is remote-friendly! Qualifications - Minimum 4+ years of experience with C# and .NET / ASP.NET Core - Experience building data pipelines or data processing solutions - Solid knowledge of SQL and relational databases - Hands-on experience with Azure cloud platform - Understanding of data modeling and data transformation concepts - Experience with REST APIs and backend development - Experience working with Git and CI/CD workflows - Fluent English (spoken and written - remote-first working environment) Requirements - Experience with Scala / Spark / Databricks pipelines - Experience with event-driven architecture or messaging systems - Knowledge of microservices architecture and distributed systems - Exposure to AI/LLM-based data processing (RAG, document indexing, AI search) - Experience with Docker / Kubernetes (AKS) - Familiarity with Azure Service Bus / messaging systems Responsibilities - Design and develop data processing pipelines and backend services using .NET - Build and maintain ETL/ELT workflows for data ingestion, transformation, and storage - Work with Azure data platform services to process and analyze large-scale datasets - Develop and optimize APIs for accessing and managing data - Integrate multiple data sources and third-party services - Participate in data architecture and system design decisions - Ensure performance, reliability, and scalability of data solutions - Collaborate with engineers, analysts, and data teams - Contribute to CI/CD pipelines and deployment processes Benefits - Fully remote position, with the option to work from our office in Lviv, Ukraine, if preferred - Flexible working hours to help maintain work-life balance - Be part of an English-speaking, multinational environment where you can share your expertise and learn from colleagues across different countries - PTO and sick leave to support your well-being - Support for learning and professional development expenses - Work on projects powered by modern technologies, guided by an experienced and skilled team - Opportunities to enhance both technical and interpersonal skills by joining technical communities, contributing to pre-sales activities, exploring roles as an interviewer or speaker, and participating in company-organized workshops on professional and soft skills - Transparent communication and processes that foster trust and collaboration - A cozy, friendly, and fun atmosphere that makes work enjoyable - …and so much more!
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SQL, ETL, AI, Apache Spark, Python, Data Engineering
