Senior Data Architect and Database Engineer (Hands on)

Stratus is a company that offers advanced MEP specific solutions to optimize contractor workflows. They are seeking a Senior Data Architect and Database Engineer to assess and transform their data operations, focusing on MongoDB performance and implementing best practices for data architecture and governance.

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

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
• 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