Job Description & Details
This is a heavy-duty polyglot database administration role where you will juggle relational databases, modern cloud data warehouses, document stores, and lakehouse formats. If you enjoy jumping between query optimization in PostgreSQL, tuning virtual warehouses in Snowflake, and managing document schemas in MongoDB, this gig offers a great variety of technical challenges.
What You'll Actually Be Doing
Your days will be split between keeping production lights on and helping engineering teams build scalable data pipelines. You will spend a good chunk of time analyzing execution plans for slow queries, dealing with locking and concurrency issues, and ensuring high availability across multiple distinct database ecosystems. You'll also partner directly with data engineering to wrangle modern data lakehouse patterns using Apache Iceberg, making sure schemas and storage formats don't become a bottleneck for analytics.
The Core Tech Stack
You need deep, production-level battle scars with Cloud SQL for PostgreSQL, Snowflake, and MongoDB. Knowing how to write and tune complex SQL is table stakes, but you also need to understand Snowflake's cost optimization mechanics and virtual warehouse tuning. On top of that, familiarity with Apache Iceberg table formats, snapshots, and partition evolution is critical as the company modernizes its analytical data storage layers.
Interview Expectations
Expect the hiring team to hand you a deliberately broken PostgreSQL query with a terrible execution plan and ask you to fix it live while explaining indexes and scan types. They will also likely drill into your MongoDB data modeling strategy, asking how you decide when to embed documents versus referencing them, and how that impacts sharding and memory usage under high-throughput workloads.
Application Advice
Skip the generic resume summary and put your database platforms front and center. Explicitly highlight terms like 'query optimization', 'execution-plan analysis', 'Snowflake virtual warehouse tuning', and 'Apache Iceberg' so the ATS flags you immediately. If you have managed petabyte-scale datasets or rescued a failing production cluster under pressure, make sure those specific impact metrics are visible in your bullet points.