Database Reliability Engineer

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.

Data Reliability & Infrastructure

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.

Cross-Team Support

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.

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 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.

X-Factor: AI-Native Engineering

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.

Your Journey After Applying

Stage 1 — TA Interview

Stage 2 — Screening Questionnaire

Stage 3 — Hiring Manager Interview

Stage 4 — Head of IT Interview

Why Join NEXT

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.