Lead Data Engineer
马拉布斯是一家全球人工智能和数字解决方案咨询公司，在人工智能，数据，分析和产品工程领域提供了智能解决方案。自2000年以来，我们与全球一些最大的医疗保健，生命科学，金融服务和政府组织合作。随着我们的全球 footprint 不断扩大，我们有了一个令人兴奋的机会让一个高技能的领先数据工程师加入我们的创新和动态团队。领先数据技术人员。作为领先数据工程师，你将负责设计，构建和管理组织的现代数据平台，确保可靠，安全和可扩展的数据产品支持分析，报告和人工智能驱动的业务举措。你将领导企业数据管道，湖泊建筑和治理框架的开发，同时与 AI/ML,平台工程和安全团队密切合作。理想候选人结合了数据工程，数据建模，云架构和数据治理方面的深入专业知识，重点是可靠性，可观察性和监管合规性。领先数据技术人员日复一日。· 设计，构建和维护可扩展的湖泊建筑和企业数据平台，支持分析，报告和人工智能驱动解决方案。· 开发和管理安全的数据摄入框架，包括 CDC,批量，API 以及从运营和交易源系统中基于文件的集成。· 创建并维护能够实现一致，可靠和商业准备的数据消费的数据模型，语义层和规范测量表格。· 实施数据质量，调整，可观察性和监控流程以确保可靠，可恢复和高性能的数据管道。· 与 AI/ML,平台工程和安全团队合作，提供受管理的数据产品，支持监管合规要求，确保适当的数据分类和访问控制。· 建立并执行数据治理标准，血统文件，数据契约，质量门和运营程序，同时支持新数据来源和环境的集成。领先数据工程师：技能与经验 · 8年以上在数据工程领域的经验，包括端到端拥有数据摄入，转型，存储和分析交付，具有经验领导大规模数据举措。· 强大的 Python 和 SQL 专业知识，包括先进数据建模，转换信息
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Marlabs, a global AI and Digital Solutions Consulting firm, delivers intelligent solutions across AI, data, analytics, and product engineering. Since 2000, we have partnered with some of the largest healthcare, life sciences, financial services, and government organizations worldwide. As we continue to expand our global footprint, we have an exciting opportunity for a highly skilled Lead Data Engineer to join our innovative and dynamic team.
Lead Data Engineer | About You
As a Lead Data Engineer, you will be responsible for designing, building, and managing the organization's modern data platform, ensuring reliable, secure, and scalable data products that support analytics, reporting, and AI-driven business initiatives. You will lead the development of enterprise data pipelines, lakehouse architecture, and governance frameworks while partnering closely with AI/ML, platform engineering, and security teams. The ideal candidate combines deep expertise in data engineering, data modeling, cloud-based architectures, and data governance with a strong focus on reliability, observability, and regulatory compliance.
Lead Data Engineer | Day-to-Day
- Design, build, and maintain scalable lakehouse architectures and enterprise data platforms that support analytics, reporting, and AI-driven solutions.
- Develop and manage secure data ingestion frameworks, including CDC, batch, API, and file-based integrations from operational and transactional source systems.
- Create and maintain data models, semantic layers, and governed metrics that enable consistent, trusted, and business-ready data consumption.
- Implement data quality, reconciliation, observability, and monitoring processes to ensure reliable, recoverable, and high-performing data pipelines.
- Partner with AI/ML, platform engineering, and security teams to deliver governed data products, support regulatory compliance requirements, and ensure proper data classification and access controls.
- Establish and enforce data governance standards, lineage documentation, data contracts, quality thresholds, and operational procedures while supporting onboarding of new data sources and environments.
Lead Data Engineer | Skills & Experience
- 8+ years of experience in Data Engineering, including end-to-end ownership of data ingestion, transformation, storage, and analytics delivery, with experience leading large-scale data initiatives.
- Strong expertise in Python and SQL, including advanced data modeling, transformation frameworks, data quality management, and performance optimization.
- Hands-on experience with modern lakehouse architectures and data platforms, including technologies such as Apache Iceberg, Delta Lake, Hudi, object storage, and cloud-native data solutions.
- Proven experience building scalable data pipelines and CDC solutions, leveraging technologies such as Kafka, Debezium, Airflow, Dagster, dbt, and enterprise integration frameworks.
- Strong understanding of data governance, lineage, security, and compliance practices, including data contracts, access controls, observability, audit readiness, and regulated industry environments.
- Experience collaborating with AI/ML, analytics, platform engineering, and business teams to deliver trusted, governed, and scalable data products; financial services or banking industry experience is highly preferred.
Originally posted on Himalayas