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Senior Data Engineer Jobs

Built from a 20M+ job database and updated hourly, this list surfaces senior data engineer roles across pipelines, warehousing, architecture, orchestration, and cloud data platforms.

Openings may be titled senior or lead data engineer, analytics engineer, data platform engineer, data architect, streaming engineer, or data engineering manager. Work can include designing batch and real-time pipelines, modeling warehouse or lakehouse data, operating orchestration systems, improving lineage and data quality, governing access, and building platforms used by analysts, scientists, product teams, or external customers. In a product team, engineers may own event collection and operational data services; centralized platform groups often emphasize reusable infrastructure and standards; regulated employers may add privacy, retention, and audit controls. Judge seniority by architectural authority, production accountability, scale and criticality of datasets, influence on upstream and downstream teams, and responsibility for mentoring or management. A staff-level platform specialist and a people manager may have similar titles but very different hiring signals and daily work.

Create a small comparison matrix for platform users, data latency, architecture ownership, and operational support. Those fields expose major differences between an analytics-engineering role, a domain pipeline team, and a shared data platform. Add the cloud, warehouse or lakehouse, orchestration, streaming, transformation, and programming stack from the original posting, along with governance and on-call duties. Also record whether the team owns source ingestion, semantic models, serving layers, or the full path. A stated assessment may cover SQL, coding, modeling, distributed systems, or pipeline design; prepare only after checking the described process. Employer, location, compensation, and sponsorship filters handle practical constraints. Weigh disclosed pay and equity against platform maturity, incident burden, team structure, and architecture authority, and confirm all current requirements at the source.

Browse and filter the data-engineering inventory for free. With an optional free account, save the strongest matches, record application stages, and use Copilot where helpful. Keep platform and ownership notes tied to each application.

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This list covers senior, staff, and principal data engineering roles across data platforms, pipelines, infrastructure, warehouses, ETL, analytics engineering, and data architecture.

Describe the scale, reliability, latency, and cost of systems you owned. Quantify improvements to data quality, processing time, uptime, developer productivity, or cloud spend where possible.

Common requirements include SQL, Python or JVM languages, data modeling, orchestration, streaming, warehouses or lakehouses, cloud platforms, observability, testing, and infrastructure automation.

Expect SQL and coding exercises, data-modeling questions, pipeline and platform design, reliability scenarios, and discussions of tradeoffs involving scale, freshness, quality, governance, and cost.

Data engineering often owns ingestion, storage, processing, and platform reliability. Analytics engineering usually focuses on transforming trusted warehouse data into tested, documented models for analysis and reporting.

Usually they are senior individual contributors who guide architecture and execution across teams. Management titles generally add hiring, coaching, staffing, and performance responsibilities.

Ask about ownership, lineage, testing, observability, incident response, deployment practices, access controls, documentation, cost visibility, and how quickly teams can safely publish new datasets.

Compare system scale, technical debt, on-call load, platform roadmap, team boundaries, stakeholder expectations, cloud costs, data governance, and the authority you will have over architecture and standards.