Lead Data Engineer

Marlabs is a global AI and Digital Solutions Consulting firm delivering solutions across AI, data, analytics, and product engineering. The Lead Data Engineer will design, build, and manage modern data platforms, enterprise data pipelines, lakehouse architectures, and governance frameworks that support analytics, reporting, and AI-driven initiatives. The role also partners with AI/ML, platform engineering, and security teams to ensure reliable, secure, compliant, and scalable data products.

Responsibilities:

• 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

Requirements:

• 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