数据工程师 IV
Data Engineer IV
描述
*根据NERC法规，此职位要求申请者为美国公民、绿卡持有者或拥有永久居留权。*
ROI Agency与一家知名客户合作，正在寻找一名远程数据工程师IV，该职位由我们已成功支持数年的团队填补。
这是一项需要评估执行层代码能力的实践性工程职位。
数据工程师IV
职位概述
首席数据工程师/架构师（数据工程师IV）是负责定义企业级数据架构、平台策略和治理标准的高级技术领导者。该职位决定了数据在所有应用程序和业务领域中如何被收集、建模、处理、保护和使用，确保组织数据生态系统的长期可扩展性、可靠性和性能。
首席数据工程师推动大规模现代化、湖仓架构、主数据管理（MDM）的采用、元数据自动化、Delta Lake策略、多云集成以及端到端数据平台的演进。该职位具有完全自主权，与总监、高级架构师和跨职能领导密切合作，指导影响企业系统、分析、合规性和技术投资的决策。
该职位兼具战略性和实践性——解决最复杂的工程技术问题，创建可重用框架，并指导高级工程师提升整个企业的数据工程成熟度。
主要职责：
- 负责企业数据生态系统的长期设计和架构，包括数据采集、存储、建模、血缘关系、治理和分析层。
- 设计可扩展的湖仓、Delta Lake和分布式数据架构，以支持高级分析、操作流程以及跨业务领域的集成。
- 领导企业范围内的现代化项目：仓库迁移、领域建模重构、治理提升、流处理采用或跨云数据集成。
- 在所有数据团队中定义并执行数据建模、血缘关系、元数据、MDM、质量、安全和合规标准。
- 创建可重用的架构模式、框架、编排和平台组件，并在各工程组中推广使用。
- 解决最复杂的技术问题，包括分布式系统瓶颈、数据质量危机、血缘关系缺口和多领域数据对账问题。
- 指导计算、存储和
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Description
*Due to NERC regulations US Citizenship, Green Card Hold, or Permanent Residency is required for this role.*
ROI Agency is partnered with an established client to fill a remote Data Engineer IV position on a team we have successfully supported for a few years.
This is hands-on engineering position requiring the ability evaluate execution layer code.
Data Engineer IV
Position Summary
The Principal Data Engineer / Architect (Data Engineer IV) is a senior technical leader responsible for defining the enterprise-wide data architecture, platform strategy, and governance standards. This role shapes how data is collected, modeled, processed, secured, and consumed across all applications and business domains, ensuring the long-term scalability, reliability, and performance of the organization’s data ecosystem.
Principal Data Engineers drive large-scale modernization, lakehouse and warehouse architecture, MDM adoption, metadata automation, Delta Lake strategy, multi-cloud integrations, and end-to-end data platform evolution. Operating with full autonomy, this role engages with Directors, senior architects, and cross-functional leaders to guide decisions that impact enterprise systems, analytics, compliance, and technology investments.
This position is both strategic and hands-on when needed—solving the hardest technical problems, creating reusable frameworks, and mentoring senior engineers to elevate overall data engineering maturity across the enterprise.
Essential Functions:
- Own the long-term design and architecture of the enterprise data ecosystem, including ingestion, storage, modeling, lineage, governance, and analytics layers.
- Design scalable lakehouse, Delta Lake, and distributed data architectures supporting advanced analytics, operational workflows, and integration across business domains.
- Lead enterprise-wide modernization projects: warehouse migrations, domain modeling redesigns, governance uplift, streaming adoption, or cross-cloud data integrations.
- Define and enforce standards for data modeling, lineage, metadata, MDM, quality, security, and compliance across all data teams.
- Create reusable architectural patterns, frameworks, orchestrations, and platform components adopted across engineering groups.
- Solve the most complex technical problems, including distributed system bottlenecks, data quality crises, lineage gaps, and multi-domain data reconciliation issues.
- Guide cost optimization strategy for compute, storage, and orchestration workloads across the data platform.
- Partner with enterprise architecture, analytics, InfoSec, product, and application engineering to ensure alignment with organizational strategy.·
- Influence leadership decisions regarding data strategy, platform investments, tooling, and sprint/roadmap priorities.
- Mentor senior engineers, conduct design reviews, and provide technical leadership across teams to raise the overall engineering bar.
Basic Qualifications:
- Bachelor’s degree in CS/IT/Data Science or equivalent experience (Master’s preferred).
- 10+ years experience in data engineering, data architecture, or distributed systems engineering.
- Proven track record designing and implementing enterprise-scale data platforms with Lakehouse/Delta architectures.
- Expert-level proficiency with SQL, Spark, Python, Databricks, Delta Lake, Azure Data Factory, and distributed processing.
- Deep understanding of data modeling (conceptual, logical, physical), governance frameworks, MDM, metadata catalogs, and lineage systems.
- Experience leading multi-team engineering initiatives and influencing architectural decisions at the leadership level.
- Strong grounding in security, compliance, data privacy, and regulatory data handling.
Originally posted on Himalayas