Lead Data Engineer

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.

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.

• 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.

• 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.