Senior Database Engineer

As a Senior Database Engineer at Tekion, you will design, automate, and optimise database operations across our cloud-native automotive retail platform, connecting OEMs, dealerships, partners, and consumers. You’ll be working on scaling problems, laying down processes and streamlining requirements to actual executable tasks.

You’re expected to know in-depth about different storage and replication internals, trade-offs across SQL/NoSQL technologies, distributed systems fundamentals, transaction interaction and concurrency. A combination of this with strong Python fundamentals and Python scripting experience would make you a strong candidate for this role.

You should have at least 4+ years of experience. You should know about MongoDB in depth, and at least one SQL database like MySQL, MariaDB, PostgreSQL. People who want to build systems, not just manage them.

 

Key Responsibilities:

1. Core Database Engineering & Distributed Systems (Primary Focus):
1. Debug and prevent concurrency issues (deadlocks, lock escalation, starvation, long-running transactions) and advise teams on safe patterns (idempotency, retries, backoff, fencing tokens).
2. B-tree/LSM trade-offs, page layout, WiredTiger cache, journaling, WAL/redo/undo, checkpoints, compaction, MVCC behaviour
3. Backups (Logical vs Physical vs Snapshots based), upgradation of database systems and versions, data migration, and Disaster Recovery implementation
4. Query planner behaviour, statistics, cardinality estimation pitfalls
5. Indexing strategies and write amplification trade-offs
2. Reliability Engineering, Observability & Incident Response :
   Build and implement database reliability practices:

1. Lead incident response and postmortems; implement preventative controls and automated remediation.
2. Implement monitoring and alerting for replication lag, lock contention, buffer/cache health, slow query patterns, storage growth, and failover events.

1. Python Automation & Platform Tooling (Required):
   Develop and maintain robust Python tooling/services for:

• Automated health checks, failover verification, backup/restore validation, and consistency checks.
• Online schema change orchestration, safe rollout/rollback workflows, and guardrails in automation.
• Performance diagnostics automation (plan capture, workload fingerprinting).
• Build scalable automation that handles millions of records/events efficiently (attention to time/space complexity).

1. Data Modelling Across SQL & NoSQL:
   Guide data modelling decisions across relational and document/Key-Value stores:

• Normalisation vs denormalisation trade-offs
• Secondary index design, hot partition mitigation, and throughput planning
• TTL, archiving, and lifecycle policies
• Define governance for data correctness, durability, and operational safety in high-throughput systems

1. Collaboration, Standards & Enablement

1. Partner with application teams to design safe data access patterns, migration strategies, and operational standards.
2. Define best prac