Engineering Manager, Data Platform
S
SentiLinkFintech
US-basedFull-TimeManager
Salary200000 - 240000 USD per year
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Job Details
- Experience
- 2-5+ years
- Required Skills
- AWSDockerPostgreSQLPythonSQLETLKafkaGoRDBMSSparkRedshiftAWS Lambda
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
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
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