[Hiring] Engineering Manager, Data Platform @SentiLink

Role Description

We’re looking for an Engineering Manager to lead our Data Platform team, responsible for building and scaling the data infrastructure that powers SentiLink’s products and decisioning systems. This team owns the pipelines, storage systems, and data services that enable all of our products, data analysis, and business intelligence. These systems are foundational to how we detect fraud, power machine learning models, and deliver insights to customers.

As the manager of this team, you will:

• Lead a group of engineers responsible for designing and operating scalable data systems—from ingestion and transformation to serving and analytics.
• Partner closely with Data Science, Product, and Engineering teams to ensure data is reliable, accessible, and actionable across the company.
• Define technical direction, improve the reliability and scalability of our data systems, and help evolve our data platform to support the company’s next stage of growth.
• Engage deeply with technical systems—guiding architecture, reviewing designs, and helping solve complex data challenges.

This is a remote, US-based position.

Technologies: Golang, Python, PostgreSQL (RDS), Redshift, EMR, Spark, Docker, OpenSearch, AWS technologies (Lambda, Cognito and other standard services)

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.) preferred.
• Familiarity with data lakes, warehouses (Redshift), and modern data architectures preferred.
• Experience building data platforms that support ML or fraud/risk systems preferred.
• Fintech or fraud domain experience preferred.

Compensation

• $200,000/year - $240,000/year + equity + benefits

Perks

• Employer paid group health insurance for you and your dependents.
• 401(k) plan with employer match (or equivalent for non US-based roles).
• Flexible paid time off.
• Regular company-wide in-person events.
• Home office stipend, and more!

Corporate Values

• Follow Through
• Deep Understanding
• Whatever It Takes
• Do Something Smart