Senior Data Architect and Database Engineer (Hands on)

Stratus, deriving from the Latin term meaning 'layer', offers an advanced set of MEP specific solutions that seamlessly layer across a contractor's entire workflow from design to fabrication to installation. Our team of seasoned industry experts, skilled technology leaders, innovators, and entrepreneurs understands that fabrication does not occur in isolation, and increasingly, it may not happen within your own fabrication shop. Through close relationships with our customers—who include some of the most innovative and largest MEP contractors—we have developed a suite of Stratus tools to digitize, automate, and optimize piping, plumbing, sheet metal, and electrical contracting. Stratus provides the software layer an MEP Contractor needs to optimize profits with true "Data Driven Contracting." GENERAL DESCRIPTION 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. KEY RESPONSIBILITIES Assessment & Strategy 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. Modernization & Implementation 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 Technical Leadership & Delivery 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 AI-Enabled Data Platform 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 QUALIFICATIONS 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 HAVE 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 WHAT SUCCESS LOOKS LIKE Success in this role means a measurably more reliable, performant, and maintainable MongoDB platform — fewer incidents, faster queries, healthier indexes, cleaner schema, and operational runbooks the team actually uses. Beyond the database tier, you'll have driven meaningful progress on modernizing our broader data infrastructure, with a clear roadmap and momentum toward the future-state architecture. Your impact will show up in data quality, pipeline reliability, and team velocity. BENEFITS Comprehensive and competitive health benefits plan Matching 401k contributions 20 days annual PTO Primarily remote work with occasional annual team onsites This is a fully remote position open to candidates based in the United States. Compensation: ~$210,000 base salary, commensurate with experience, plus benefits.