Job Description
Overview The Lead Data Engineer on the Nebula team plays a significant technical leadership role in shaping and scaling the data foundation that powers analytics, reporting, AI development, and operational decision-making across the organization. This role combines hands-on data engineering execution with practical team leadership, helping the organization build reliable, flexible, and production-ready data systems. The Lead Data Engineer heads a lean, high-caliber squad of data engineers, while remaining deeply hands-on in the design, development, and operation of core data systems. The role balances direct technical contribution with mentoring, coaching, coordination, and day-to-day support for the engineers on the squad. Working across ingestion, transformation, storage, modeling, orchestration, and delivery, this role partners closely with Product, Engineering, AI, Analytics, and domain Subject Matter Experts (SMEs) to translate complex business processes into scalable data platforms, pipelines, and trusted datasets. This role owns the technical direction for core data capabilities, including ETL/ELT, batch and real-time processing, OLTP and OLAP systems, BI-ready data models, and cloud-based data infrastructure in a regulated, high-stakes environment. Success requires strong architectural judgment, operational discipline, and the ability to raise the technical bar for both systems and people. 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 Strategic Technical Leadership Own the architecture and evolution of core data systems, including ingestion, transformation, orchestration, storage, modeling, and delivery layers Set technical direction for ETL/ELT, batch processing, real-time pipelines, OLTP and OLAP systems, and BI-ready data assets Make pragmatic architecture decisions that balance scalability, reliability, security, performance, cost, and delivery speed Establish engineering standards, reusable patterns, and design principles that improve quality and leverage across the data platform Hands-On Data Engineering Delivery Lead the design, build, rollout, and operations of greenfield data infrastructure Build and maintain complex data pipelines across diverse source and destination systems, including databases, APIs, files, SaaS platforms, event streams, and internal applications Design and optimize data models, warehouse schemas, semantic layers, and curated datasets for analytics, reporting, AI, and product use cases Contribute directly to critical implementation work, including writing code, code and design reviews, migrations, reliability improvements, and production issue resolution Squad Leadership & Management Lead a lean, high-caliber squad of data engineers, spending focused time mentoring, coaching, managing, and coordinating the team Develop engineers through regular feedback, technical guidance, code reviews, career support, and clear expectations around quality and ownership Help prioritize team work, clarify scope, remove blockers, and ensure the squad delivers reliably against business and technical goals Contribute to hiring, onboarding, performance development, and team operating rhythms as the data engineering function grows Cloud Platform & Production Operations Deploy, operate, and improve data pipelines, data stores, and supporting infrastructure on major cloud platforms such as AWS, GCP, or Azure Drive strong practices for CI/CD, infrastructure-as-code, automated testing, monitoring, alerting, and incident response Ensure data systems are observable, fault-tolerant, recoverable, and maintainable in production Identify opportunities to reduce operational toil, improve platform reliability, and manage cloud infrastructure costs effectively Data Quality, Governance & Trust Define and enforce standards for data quality, validation, reconciliation, lineage, schema evolution, metadata, and documentation Establish patterns for data contracts, ownership, SLAs, and runbooks that help downstream teams trust and use data confidently Partner with security, compliance, and business stakeholders to support privacy, auditability, access controls, and regulated data handling Raise the maturity of data governance and reliability practices without slowing down pragmatic delivery <