Data Reliability Engineer

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. Across offices in Bangladesh, Malaysia, Sri Lanka, Cyprus, and Dubai, we build and operate the technology that lets traders access global markets at scale. 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. Working within the Platform Engineering squad, you execute the scaling and optimization initiatives designed by the squad lead — and you are the reason our databases stay fast, durable, and cost-efficient as trading data and traffic grow.

How You'll Make An Impact

Data Performance & Scalability

• Execute database optimization and scaling initiatives — implement partitioning strategies, sharding schemes, and replication configurations; independently diagnose and fix slow queries across the platform.
• 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 (trade logs, transaction data, audit trails) from production tables to cold storage (S3/Parquet/Athena/Glacier) while preserving query access for compliance and reporting.
• Implement and maintain data lifecycle automation — TTL policies, scheduled archival jobs, partition rotation, and retention enforcement.
• Conduct regular performance profiling across the data tier — identify bottlenecks in MySQL/PostgreSQL and the caching layer, then implement measurable fixes.
• Support load testing and capacity planning for the data layer — analyze growth trends, identify breaking points, and implement fixes before traffic growth causes production issues.
  
  
  

• Implement and maintain data-tier BC/DR infrastructure — multi-AZ deployments, read replicas, Aurora Global Database/cross-region replication, backup schedules, and failover triggers targeting RTO/RPO under 5 minutes; participate in regular DR drills.
• 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 — query latency, availability, replication health — and drive improvements against them.
• Implement monitoring, alerting, and observability for the data tier — dashboards, slow-query alerting, and replication/cache health monitoring targeting fast MTTD on data incidents.
• 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.
• Remediate data-related security findings from the Cyber Security Squad — implement fixes and verify through retesting.
• Write and maintain infrastructure-as-code for data resources — Terraform or equivalent for reproducible, version-controlled provisioning.
• Document runbooks, archiving procedures, and operational guidance so any engineer can respond to data-tier incidents with clear guidance.
  
  
  

• 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 for compliance and reporting.
• 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, reproducible way.
• Can write and debug stored procedures and dynamic partitioning logic for database-level automation.
• Evidence-driven — profiles and measures before guessing; validates every optimization against before/after data.
• Performance-oriented — treats data-tier latency as a first-class reliability problem, not a tuning afterthought.
• Good communicator — works with product squads to shape schemas and access patterns, and explains data constraints clearly.
  
  
  

• You actively use modern AI agentic workflows daily — not limited to Copilot autocomplete.
• You are proficient with Claude Code, Cursor, Windsurf, or equivalent tools.
• You are comfortable with project-level AI configuration (CLAUDE.md, rules files), agentic task delegation, and AI-driven code review.
• You think in terms of 5–10x productivity through AI-augmented development — and you can demonstrate it.
  
  
  

• Stage 1 — TA Interview
• Stage 2 — Screening Questionnaire
• Stage 3 — Hiring Manager Interview
• Stage 4 — Head of IT Interview
  
  
  

• Work on data challenges at real scale — 100M+ row tables, 1TB+ databases, high-frequency trading transaction volumes.
• A team that treats database performance and data-tier reliability as first-class engineering problems, not DBAs on the side.
• Flat structure — your work directly shapes the platform's data infrastructure, not filtered through layers of process.
• Offices across Bangladesh, Malaysia, Sri Lanka, Cyprus, and Dubai — a genuinely global engineering team.
• Competitive compensation benchmarked to your market, with room to grow as the team scales.