Senior Data Architect and Database Engineer (Hands on)

Stratus

4d•Remote

About The Position

The Data Architect & Strategy Lead will assess our current homegrown data operations, our production database layer, and the surrounding data model — then architect and execute a transformation that brings best-in-class performance, reliability, and maintainability to our most critical systems. Our core transactional store runs on MongoDB Atlas, and this role owns its health: indexing strategy, query and aggregation tuning, schema design, replication posture, and the day-to-day operational maintenance that keeps it fast and stable. Around that core, you'll lead the broader transition from custom-built tooling to industry-standard data transformation, orchestration, and cloud-native data platforms, while ensuring reliability and scalability improve continuously throughout the journey.

Requirements

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) — index design, query profiling, aggregation framework, schema modeling, sharding, and replica sets. Expertise, resolving performance issues (runaway queries, lock contention, etc.) and putting durable preventive controls in place.
Hands-on experience with vector search and embeddings pipelines in production (Atlas Vector Search, pgvector, or equivalent)
Demonstrated use of AI-assisted development tools (Claude Code, Copilot, Cursor) for database and data pipeline work — query tuning, schema design, migration scripting
Experience designing data architecture that supports RAG, semantic search, or agentic AI workloads
PostgreSQL experience, including indexing strategy, query tuning via EXPLAIN/ANALYZE, schema design, and operational maintenance (replication, backups, autovacuum, connection pooling)
Demonstrated ability to partner with application engineers on performance — reviewing queries and data-access patterns in code, informing design decisions, and contributing to engineering discussions in a hands-on advisory capacity
Hands-on experience designing and implementing data lakes, data pipelines, ELT/ETL pipelines at scale
Demonstrated ability to create incremental migration strategies that minimize disruption while delivering continuous value
Experience with cloud platforms (Azure, AWS, or GCP) and cloud-native data services
Strong understanding of data quality, testing, and monitoring practices, including database-tier observability and alerting

Nice To Haves

MongoDB certification (Associate DBA, Associate Developer, or higher) and/or substantive MongoDB University coursework
Experience operating MongoDB Atlas at scale: cluster-tier transitions, online archive, Atlas Search, BI Connector, cross-region replication, and Atlas-native security controls
Experience operating PostgreSQL on Azure (Azure Database for PostgreSQL Flexible Server), including high-availability configurations, point-in-time restore, and read replicas
Experience with logical replication, change-data-capture (Debezium, MongoDB Change Streams), and cross-engine sync patterns
Experience with Azure ecosystem (Azure Data Factory, Synapse Analytics, Azure Functions, Event Grid)
Experience with BigData, DynamoDB, Data marts
Experience with real-time data processing and event-driven architectures
Knowledge of data governance frameworks and compliance requirements (SOC 2)
Experience mentoring data engineers and application engineers on modern practices, tooling, and database usage patterns

Responsibilities

Conduct comprehensive review of our existing MongoDB Atlas deployment, homegrown data operations, pipelines, and data models
Identify technical debt, bottlenecks, and areas requiring immediate attention versus long-term improvement, with explicit focus on database-layer reliability
Design future-state architecture leveraging MongoDB best practices alongside modern data stack technologies (transformation frameworks, orchestration platforms, cloud data warehouses, etc.)
Create tactical and strategic roadmaps that deliver incremental value while building toward the target architecture
Establish data architecture standards and governance practices.
Own MongoDB performance optimization end-to-end: index strategy, query and aggregation-pipeline tuning, schema refactoring, shard-key design, read/write concern tuning, and cluster-tier capacity planning
Lead ongoing MongoDB maintenance: version upgrades, patching, backup and restore strategy, disaster-recovery rehearsals, and Atlas configuration hygiene
Lead migration from homegrown tooling to best-in-class data engineering platforms and frameworks
Design and implement modern data pipelines, transformations, and orchestration workflows that integrate cleanly with our MongoDB transactional store
Balance "build vs. buy" decisions with focus on leveraging proven solutions over custom development
Drive hands-on implementation of critical data infrastructure improvements, including MongoDB index rollouts, runaway-query mitigation, and proactive stabilization
Establish testing, monitoring, and data quality frameworks for production systems — including MongoDB-specific observability (Atlas Performance Advisor, Query Profiler, Atlas alerts, custom Grafana/Prometheus dashboards) and clear, actionable runbooks
Mentor engineers on modern data practices, MongoDB-idiomatic patterns (document modeling, aggregation framework, change streams), and architectural patterns; raise the team's database-engineering bar
Architect the data layer to support AI-driven workloads: vector search, embeddings pipelines, RAG retrieval patterns, and real-time index updates via change streams
Use AI tooling aggressively as a force multiplier — LLM-assisted query review, index recommendations, schema refactoring, runbook generation, and agent-assisted hands-on tuning
Establish governance for AI-driven data access: query cost controls, read-path safety, and observability for agent workloads against production stores
Partner with application and ML engineering to make production data AI-ready: clean modeling, documented lineage, and retrieval-friendly schema design