[Hiring] Lead Data Engineer @Marlabs

Role Description

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.

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.

Qualifications

• 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.