Senior Engineering Manager, Data Platform

SentiLink provides innovative identity and risk solutions, empowering institutions and individuals to transact with confidence. They are seeking a Senior Engineering Manager to lead their Data Platform team, responsible for building and scaling the data infrastructure that powers SentiLink’s products and decisioning systems.

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:

• 3 - 7 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