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Tech Stack
Tools & technologiesAWSAzureCloudETLGoogle Cloud PlatformMongoDBPostgres
About the role
Key responsibilities & impact- Assess current homegrown data operations and production database layer
- Design future-state architecture leveraging MongoDB best practices and modern data stack technologies
- Own MongoDB performance optimization end-to-end
- Lead ongoing MongoDB maintenance
- Drive hands-on implementation of critical data infrastructure improvements
- Establish testing, monitoring, and data quality frameworks
- Architect the data layer to support AI-driven workloads
- Partner with application and ML engineering to make production data AI-ready
Requirements
What you’ll need- 8+ years of experience in data engineering, data architecture, database administration, or analytics engineering with 3+ years in senior/lead roles
- Deep, hands-on MongoDB expertise at production scale (Atlas M40+ ideal)
- Expertise in resolving performance issues (runaway queries, lock contention, etc.)
- Hands-on experience with vector search and embeddings pipelines in production
- Experience designing data architecture that supports RAG, semantic search, or agentic AI workloads
- PostgreSQL experience
- Experience with cloud platforms (Azure, AWS, or GCP)
- Hands-on experience designing and implementing data lakes, data pipelines, ELT/ETL pipelines at scale
- Strong understanding of data quality, testing, and monitoring practices
Benefits
Comp & perks- Comprehensive and competitive health benefits plan
- Matching 401k contributions
- 20 days annual PTO
- Primarily remote work with occasional annual team onsites
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
MongoDBdata engineeringdata architecturedatabase administrationanalytics engineeringPostgreSQLdata lakesdata pipelinesELTETL
Soft Skills
leadershipcollaborationproblem-solvingcommunication
