Engineering Manager, Data Platform

SentiLink is an innovative company providing identity and risk solutions, and they are seeking an Engineering Manager to lead their Data Platform team. This role involves building and scaling the data infrastructure that powers SentiLink’s products and decision-making systems while ensuring data reliability and accessibility across the organization.

Responsibilities:

• Lead and grow a team of data engineers responsible for SentiLink’s data platform and infrastructure
• Define and drive the technical vision for data ingestion, processing, storage, and serving systems
• Design and evolve scalable data pipelines (batch and real-time) to support product and data science use cases
• Ensure high standards for data quality, reliability, and observability across all data systems
• Partner with Data Science to enable their pipelines and enable efficient access to high-quality data
• Collaborate with Product and Engineering teams to power data-driven features and decisioning systems
• Work closely with Infrastructure to optimize performance, cost, and scalability of data systems
• Establish best practices for data governance, schema management, and pipeline reliability
• Support operational excellence, including monitoring, alerting, and incident response for data systems
• Hire, mentor, and develop engineers while maintaining a high hiring bar
• Foster a culture of ownership, accountability, and continuous improvement
• Contribute to architecture and technical problem-solving as needed

Requirements:

• 2-5+ years of engineering management experience leading data or platform teams
• Strong background as a senior or staff-level data engineer or backend engineer with data focus
• Experience building and scaling data platforms, including ETL/ELT pipelines and data infrastructure
• Proficiency in Python, Golang, or similar languages
• Deep understanding of distributed data systems, batch and streaming architectures
• Experience with cloud-based data platforms (AWS, GCP, or Azure)
• Strong knowledge of databases, data modeling, and query optimization (SQL, RDBMS, data warehouses)
• Track record of delivering reliable, scalable data systems in production environments
• Comfortable leading technical discussions and diving into system design details
• Experience operating in fast-paced, startup or growth-stage environments
• Experience with big data and streaming technologies (Spark, Kafka, Flink, etc.)
• Familiarity with data lakes, warehouses (Redshift), and modern data architectures
• Experience building data platforms that support ML or fraud/risk systems
• Fintech or fraud domain experience