Senior Database Engineer

About Tekion:

Positively disrupting an industry that has not seen any innovation in over 50 years, Tekion has challenged the paradigm with the first and fastest cloud-native automotive platform that includes the revolutionary Automotive Retail Cloud (ARC) for retailers, Automotive Enterprise Cloud (AEC) for manufacturers and other large automotive enterprises and Automotive Partner Cloud (APC) for technology and industry partners. Tekion connects the entire spectrum of the automotive retail ecosystem through one seamless platform. The transformative platform uses cutting-edge technology, big data, machine learning, and AI to seamlessly bring together OEMs, retailers/dealers and consumers. With its highly configurable integration and greater customer engagement capabilities, Tekion is enabling the best automotive retail experiences ever. Tekion employs close to 3,000 people across North America, Asia and Europe.

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:

Core Database Engineering & Distributed Systems (Primary Focus):

Debug and prevent concurrency issues (deadlocks, lock escalation, starvation, long-running transactions) and advise teams on safe patterns (idempotency, retries, backoff, fencing tokens).

B-tree/LSM trade-offs, page layout, WiredTiger cache, journaling, WAL/redo/undo, checkpoints, compaction, MVCC behaviour

Backups (Logical vs Physical vs Snapshots based), upgradation of database systems and versions, data migration, and Disaster Recovery implementation

Query planner behaviour, statistics, cardinality estimation pitfalls

Indexing strategies and write amplification trade-offs

Reliability Engineering, Observability & Incident Response :

Build and implement database reliability practices:

Lead incident response and postmortems; implement preventative controls and automated remediation.

Implement monitoring and alerting for replication lag, lock contention, buffer/cache health, slow query patterns, storage growth, and failover events.

Python Automation & Platform Tooling (Required):

Develop and maintain robust Python tooling/services for:

Automated health checks, failover verificatio