Data Reliability Engineer
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Key skills for this role
About the Role
NEXT Ventures is looking for a Database Reliability Engineer to ensure the data layer never becomes a bottleneck. You will own database performance, archiving pipelines, caching reliability, and BC/DR for the data tier.
Key Skills for This Role
Responsibilities
- Execute database optimization and scaling initiatives — implement partitioning strategies, sharding schemes, and replication configurations
- Own performance diagnostics on high volume tables — EXPLAIN plan analysis, index tuning, buffer cache hit ratio investigation, lock contention, and replication lag
- Build and maintain data archiving pipelines — move historical records from production tables to cold storage while preserving query access
- Implement and maintain data lifecycle automation — TTL policies, scheduled archival jobs, partition rotation, and retention enforcement
- Conduct regular performance profiling across the data tier and implement measurable fixes
- Support load testing and capacity planning for the data layer
- Implement and maintain data tier BC/DR infrastructure — multi AZ deployments, read replicas, Aurora Global Database, backup schedules, and failover triggers
- Implement and maintain caching strategies (Redis, ElastiCache) — cache invalidation logic, read through/write behind patterns, TTL hygiene, and cache hit rate monitoring
- Define and track reliability metrics (SLIs/SLOs) for the data layer and drive improvements
- Implement monitoring, alerting, and observability for the data tier
- Maintain and improve database level automation — stored procedures, dynamic partitioning scripts, and scheduled maintenance jobs
- Collaborate with application and product squads to design schemas, indexes, and access patterns that scale
Requirements
- 4–6 years of professional engineering experience, with at least 2 years in database focused infrastructure work at moderate to high scale
- Strong expertise in MySQL and PostgreSQL — EXPLAIN plan analysis, index design, partitioning, sharding, and replication setup — with direct experience on 100M+ row tables
- Able to build data archiving pipelines to cold storage (S3/Parquet/Athena/Glacier) while keeping records queryable
- Proficient with AWS managed database services — RDS and Aurora — including provisioning, read replicas, parameter group tuning, and monitoring
- Hands on with Redis and ElastiCache — cache invalidation patterns, TTL hygiene, hit rate monitoring, and incident diagnosis
- Automates archival, maintenance, and diagnostic work using Python and/or Bash
- Familiar with monitoring and observability tooling (Prometheus, Grafana, Datadog, or similar)
- Understands multi AZ and cross region DR architectures, backup strategies, and failover triggers targeting RTO/RPO under 5 minutes
- Comfortable writing Terraform or CloudFormation to provision data resources
Full Job Posting
Who We Are
- NEXT Ventures is a global fintech group powering FundedNext — one of the world's fastest growing proprietary trading platforms — and FNmarkets, a regulated CFD brokerage.
- Our Platform Engineering team owns the infrastructure, reliability, and observability backbone that every product squad depends on.
Your Role in Our Mission
- As our Database Reliability Engineer, you are the specialist who ensures our data layer never becomes the bottleneck.
- You own database performance, archiving pipelines, caching reliability, and BC/DR for the data tier that underpins all FundedNext services.
How You'll Make An Impact
- Execute database optimization and scaling initiatives — implement partitioning strategies, sharding schemes, and replication configurations; independently diagnose and fix slow queries.
- Own performance diagnostics on high volume tables — EXPLAIN plan analysis, index tuning, buffer cache hit ratio investigation, lock contention, and replication lag on 100M+ row tables.
- Build and maintain data archiving pipelines — move historical records from production tables to cold storage while preserving query access.
- Implement and maintain data lifecycle automation — TTL policies, scheduled archival jobs, partition rotation, and retention enforcement.
- Conduct regular performance profiling across the data tier and implement measurable fixes.
- Support load testing and capacity planning for the data layer.
- Implement and maintain data tier BC/DR infrastructure — multi AZ deployments, read replicas, Aurora Global Database, backup schedules, and failover triggers targeting RTO/RPO under 5 minutes.
- Implement and maintain caching strategies (Redis, ElastiCache) — cache invalidation logic, read through/write behind patterns, TTL hygiene, and cache hit rate monitoring.
- Define and track reliability metrics (SLIs/SLOs) for the data layer and drive improvements.
- Implement monitoring, alerting, and observability for the data tier.
- Maintain and improve database level automation — stored procedures, dynamic partitioning scripts, and scheduled maintenance jobs.
- Collaborate with application and product squads to design schemas, indexes, and access patterns that scale.
What You Bring
- 4–6 years of professional engineering experience, with at least 2 years in database focused infrastructure work at moderate to high scale.
- Strong expertise in MySQL and PostgreSQL — EXPLAIN plan analysis, index design, partitioning, sharding, and replication setup — with direct experience on 100M+ row tables.
- Able to build data archiving pipelines to cold storage (S3/Parquet/Athena/Glacier) while keeping records queryable.
- Proficient with AWS managed database services — RDS and Aurora — including provisioning, read replicas, parameter group tuning, and monitoring; Aurora Global Database experience is a plus.
- Hands on with Redis and ElastiCache — cache invalidation patterns, TTL hygiene, hit rate monitoring, and incident diagnosis.
- Automates archival, maintenance, and diagnostic work using Python and/or Bash.
- Familiar with monitoring and observability tooling (Prometheus, Grafana, Datadog, or similar) for slow query alerting, dashboards, and data tier incident analysis.
- Understands multi AZ and cross region DR architectures, backup strategies, and failover triggers targeting RTO/RPO under 5 minutes.
- Comfortable writing Terraform or CloudFormation to provision data resources in a version controlled manner.
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