Senior Software Engineer, Database

Pika • Palo Alto, CA

12h • Onsite

About The Position

We're looking for a Senior Database Engineer to own the data layer that powers Pika's products. As our platform scales to millions of users, we need someone who can design storage architectures that are fast, reliable, and cost-efficient — spanning relational databases, caching layers, vector search, object storage, and analytics pipelines.

You'll be responsible for schema design, query optimization, data pipeline architecture, and building the retrieval systems that power our AI features. This is a high-ownership role where you define how data flows through the entire product.

Requirements

• 5+ years of experience designing and operating databases in production at scale
• Deep PostgreSQL expertise — indexing strategies, query optimization, partitioning, JSONB, extensions
• Experience with caching systems — Redis architecture, eviction strategies, cache invalidation patterns
• Familiarity with object storage (S3 or equivalent) for media and large assets
• Understanding of vector databases (pgvector, Pinecone, Weaviate, or similar) for semantic search
• Strong data modeling skills — you can design schemas that balance normalization, performance, and developer ergonomics
• Strong SQL and query optimization skills — you read EXPLAIN plans for fun
• Experience with database reliability — replication, failover, backup/restore, monitoring
• Clear communication skills and a team-first mindset

Nice To Haves

• Experience with time-series data (activity metrics, usage analytics)
• Experience building analytics or reporting pipelines
• Familiarity with event sourcing or append-only data patterns
• Knowledge of data privacy and compliance (GDPR, data deletion workflows)
• Experience with graph databases for relationship modeling
• Previous startup experience — comfortable with ambiguity and moving fast

Responsibilities

• Design and own the data architecture — schema design, migration strategy, and storage topology for user data, media, and AI model outputs
• Build high-performance query layers — optimize reads/writes for real-time product interactions with sub-100ms targets on critical paths
• Implement vector search and retrieval systems — semantic search, embedding storage, and retrieval-augmented generation (RAG) pipelines
• Scale storage systems — capacity planning, sharding strategies, replication, and backup/recovery for growing data volumes
• Design and optimize caching layers — Redis architecture, eviction strategies, cache invalidation patterns for session state and hot data
• Build data pipelines — ETL for analytics, usage tracking, cost attribution, and operational metrics
• Ensure data reliability — backup strategies, point-in-time recovery, data integrity checks, and disaster recovery planning
• Establish data modeling best practices across the engineering team

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed