Skip to content
Senior Software Engineer – Data Persistence
| Company | Ridgeline |
|---|
| Location | New York, NY, USA |
|---|
| Salary | $140000 – $180000 |
|---|
| Type | Full-Time |
|---|
| Degrees | |
|---|
| Experience Level | Senior |
|---|
Requirements
- 5+ years of experience in software or infrastructure engineering, with a strong background in data persistence, distributed systems, or database engineering.
- Solid expertise in relational databases, particularly PostgreSQL (or similar), including query optimization, indexing, and schema design.
- Experience with NoSQL, in-memory databases, and search indexes (e.g., Redis, Elasticsearch, OpenSearch).
- Strong grasp of high availability, disaster recovery, and failover strategies (RPO/RTO).
- Experience with AWS cloud-native architectures, including services like Aurora RDS, S3, Route53, and Lambda.
- Proficiency in at least one programming language (Java,Kotlin, Python, TypeScript).
- Understanding of observability tools (DataDog), database monitoring, and performance telemetry.
- Ability to balance short-term deliverables with a long-term architectural vision.
- Excellent communication and problem-solving skills.
- A strong desire to work in a collaborative, learning-focused, and fun environment.
Responsibilities
- Guide the evolution of our data persistence architecture to meet best-in-class durability, availability, and recovery objectives.
- Architect and optimize data storage solutions for performance, scalability, and reliability across a multi-tenant, cloud-native infrastructure.
- Enhance and optimize relational databases (PostgreSQL) for query performance, indexing strategies, and schema evolution.
- Design and implement data partitioning, caching, and indexing strategies to support high-throughput, low-latency applications.
- Drive RPO/RTO improvements, ensuring high availability, failover, and disaster recovery plans are in place.
- Develop efficient data access patterns and work closely with application teams to improve performance at the application layer.
- Optimize analytics workloads, including views, materialized views, and columnar storage.
- Improve observability by investing in database monitoring, automation, and performance telemetry.
- Seek cost optimizations in database infrastructure, balancing performance and efficiency.
- Mentor engineers and contribute to a collaborative culture of technical excellence.
Preferred Qualifications
- Experience with columnar stores and big data processing frameworks.
- Background in event-driven architectures (Kafka, Pub/Sub).
- Hands-on experience with data warehousing solutions (Snowflake, BigQuery, Redshift).
- Familiarity with Kubernetes and microservices-based architectures.
