Data Engineer

Overview The Data Engineer on the Nebula team plays a critical role in building and evolving the data foundation that powers analytics, reporting, AI development, and operational decision-making across the organization. This role is responsible for designing, building, and maintaining reliable, scalable, and flexible data systems that support a wide range of internal and external use cases. Working across data ingestion, transformation, storage, modeling, and delivery, this individual partners closely with Product, Engineering, AI, Analytics, and domain Subject Matter Experts (SMEs) to translate complex business processes and data needs into production-ready data pipelines and platforms. This role contributes to the development and evolution of core data capabilities, including batch and real-time pipelines, operational and analytical data stores, semantic models, and BI-ready datasets. Success requires strong technical depth across modern data tooling, sound systems thinking, and the ability to build reliable solutions in a cloud-based, regulated, high-stakes environment. The Data Engineer is expected to operate effectively in a modern engineering environment, using automation, observability, and infrastructure-as-code practices to deploy, manage, and improve data pipelines and data platforms. In parallel, this individual will help enable downstream analytics, reporting, product capabilities, and AI systems by ensuring that data is trustworthy, accessible, and fit for purpose. This is a fully remote position that offers a competitive salary range of $220,000 to $300,000, plus an annual bonus. You'll also receive our excellent benefits package, which includes medical coverage starting on day one and a company-matched 401(k). Compensation may vary based on experience, location, and other job-related factors. Responsibilities Data Pipeline Development Design, build, and maintain robust data pipelines for a wide variety of input and output sources, including internal systems, third-party platforms, files, APIs, event streams, and databases Develop scalable ETL and ELT workflows for both batch and real-time processing Ensure pipelines are reliable, testable, observable, and easy to extend as business needs evolve Build reusable data integration patterns that support growing volumes, new source systems, and downstream consumers across analytics, applications, and AI initiatives Data Platform & Storage Design and manage data architectures that support OLTP, OLAP, and reporting workloads across operational and analytical environments Build and optimize data models, warehouse schemas, and curated datasets for analytics and BI use cases Contribute to the design and operation of modern data platforms, including warehouses, lakehouses, streaming systems, and supporting orchestration frameworks Help define patterns for data storage, partitioning, performance optimization, retention, and lifecycle management Cloud Deployment & Operations Deploy, operate, and improve data pipelines and data stores on major cloud platforms such as AWS, GCP, or Azure Use infrastructure-as-code, CI/CD, and automation practices to improve deployment speed, consistency, and reliability Monitor production data systems using logging, alerting, and observability tooling to proactively identify and resolve issues Support secure, resilient, and cost-conscious operation of cloud-based data infrastructure Data Quality, Reliability & Governance Implement data quality checks, validation rules, reconciliation processes, and monitoring to ensure trustworthy data across systems Establish and maintain standards for lineage, documentation, metadata, schema evolution, and operational runbooks Partner with stakeholders to improve data accessibility, consistency, and usability while maintaining appropriate controls and governance Contribute to practices that support security, privacy, auditability, and compliance in a regulated environment Cross-Functional Collaboration Partner closely with Product, Engineering, and business stakeholders to understand data needs, workflows, and constraints Translate business and operational requirements into clean, scalable, and maintainable data solutions Support downstream consumers of data, including analysts, researchers, product teams, and operational users Communicate clearly with both technical and non-technical stakeholders about data availability, quality, tradeoffs, and delivery timelines Iteration & Continuous Improvement Continuously improve pipeline p