Engineering Manager, Data Platform

Role:

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. You’ll partner closely with Data Science, Product, and Engineering teams to ensure data is reliable, accessible, and actionable across the company. You should also have strong data engineering fundamentals and enjoys engaging deeply with technical systems – guiding architecture, reviewing designs, and helping solve complex data challenges.

This role sits at the intersection of data engineering, platform architecture, and people leadership. You’ll 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.

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