ABOUT US
We are a US-based startup building next-generation software platforms for regulated life sciences manufacturing and laboratories. Our products operate in GxP-regulated environments, requiring strong engineering discipline, validation readiness, and security-by-design.
Here, you won’t just be a cog in the machine — you’ll be part of building the machine. If you thrive in environments where you can wear multiple hats, roll up your sleeves, and see your work through from start to finish, this is your opportunity to make a real impact.
We’re creating a culture of builders, thinkers, and problem-solvers who aren’t afraid to challenge the status quo. Your voice will be heard, your ideas will matter, and your contributions will shape the future of our product and the industry.
We’re growing fast, and looking for a hands-on Full Stack Development Lead – Life Sciences who’s ready to wear multiple hats and shape the future of digital pharma.
WHAT YOU WILL DO
BhriguOne's database estate has grown to a complexity that demands a dedicated Principal-level architect. No single engineer currently owns the cross-cutting concerns of schema governance, sharding strategy, GxP validation of the data tier, time-series performance tuning for SCADA historian loads, vector/graph stores for AI features, and the operational health of both cloud (Aurora PostgreSQL) and on-premises PostgreSQL deployments. This hire fills that gap — and shapes the data platform for the next five years.
DUTIES AND RESPONSIBILITIES:
Cloud Database — Aurora PostgreSQL & AWS
- Own Aurora PostgreSQL cluster architecture: writer/reader topology, Global Database for cross-region DR, automated failover, and PITR configuration
- Design and implement the multi-tenant sharding strategy: schema-per-tenant for isolation vs. row-level security (RLS) partitioned shards for cost efficiency at scale; Citus or logical replication for horizontal scale-out
- Define and enforce connection pooling architecture using PgBouncer (transaction mode) layered between EKS microservices and Aurora, targeting <5ms connection acquisition under peak OT workloads
- Govern Aurora Serverless v2 autoscaling policies and Reserved Instance / Savings Plan strategy across dev, staging, and production environments
- Design multi-region Active-Active or Active-Passive topology for enterprise pharmaceutical customers with data residency requirements (EU vs. US)
- Own AWS RDS Parameter Group tuning: shared_buffers, work_mem, autovacuum, WAL settings for mixed OLTP + time-series write workloads
On-Premises PostgreSQL
- Design and maintain on-premises PostgreSQL deployment for customers with air-gapped or hybrid cloud requirements (common in EU pharma and defence-adjacent biotech)
- Implement Patroni + etcd HA clusters for on-prem PostgreSQL with sub-30-second automatic failover
- Build pgBackRest backup and WAL archiving pipelines with encryption at rest, verified restores, and documented RPO/RTO for GxP validation documentation
- Define the on-prem → Aurora migration playbook: logical replication cutover, dual-write validation period, zero-downtime switchover
- Govern OS-level tuning: huge pages, vm.swappiness, NUMA topology, storage I/O scheduler for dedicated DB hosts
Database Sharding & Horizontal Scale
- Design the shard key taxonomy for BhriguOne: tenant_id as primary shard key, with composite shard keys (tenant_id + module + time_bucket) for time-series schemas
- Evaluate and select sharding technology: Citus (columnar + distributed tables) vs. application-level sharding via consistent hashing vs. PostgreSQL declarative partitioning
- Build cross-shard query routing layer that maintains ACID guarantees for GxP-critical write paths (e-signatures, batch record closure, deviation creation)
- Implement shard rebalancing strategy as tenant data volumes grow — zero-downtime resharding plan is a hard requirement
- Define shard-level backup isolation so a single tenant's backup and restore does not touch neighboring tenant data
GxP Database Design & Validation
- Own the GxP data architecture: immutable audit log tables with append-only constraints, hash-chain integrity via OpenBao transit engine, and cryptographic signing of critical record state transitions
- Author and maintain database-tier validation documents: IQ/OQ protocols for Aurora and PostgreSQL deployments, PQ scripts for data integrity under representative batch execution loads
- Implement and enforce ALCOA+ at the database layer: Attributable (created_by + session_id FK), Legible (constraint-enforced encoding), Contemporaneous (server-side timestamptz, NTP-synchronized), Original (immutable primary + versioned shadow table), Accurate (check constraints + trigger-enforced domain rules)
- Design schema change control process: all DDL migrations gated through Flyway with peer review, impact assessment, and rollback scripts; production DDL changes require a validated change record
- Define data retention policies per GxP record type: batch records (minimum 1 year post-expiry or life of product), audit logs (minimum 5 years), SCADA historian (configurable per regulation)
- Support FDA / MHRA audit readiness: produce database architecture diagrams, data flow maps, and validation summary reports on demand
Time-Series, SCADA & Historian
- Own TimescaleDB hypertable design for the SCADA historian: partitioning strategy (time + tag_id), compression policies, continuous aggregates for 1-min / 1-hour / 1-day rollups
- Tune TimescaleDB for high-ingest OT workloads: bulk insert batching, parallel workers, WAL tuning, and chunk exclusion optimization for range queries
- Design the live streaming path: Kafka → InfluxDB for sub-second real-time process parameter dashboards, with eventual consistency sync to TimescaleDB for the GxP audit-quality record
- Implement data tiering: hot data in TimescaleDB SSD tier, warm data in compressed chunks, cold data offloaded to S3 via TimescaleDB Cloud tiering or pg_partman + custom archival job
- Validate time-series data integrity for ISA-88 batch records: ensure every SCADA tag value written during a batch phase is retrievable and matches the EBR's process parameters
- Design the ISA-18.2 alarm state persistence model: alarm state machine transitions stored with nanosecond-precision timestamps for regulatory alarm management reports
Vector & Graph Databases
- Own pgvector schema design for multi-tenant RAG pipelines: embedding dimensions, HNSW vs. IVFFlat index selection, per-tenant namespace isolation using schema-level partitioning
- Govern Qdrant deployment for high-cardinality collections (SOP embeddings, batch record embeddings, deviation knowledge base) — collection strategy, payload indexing, quantization for cost control
- Design Apache Age or Neo4j graph schema for the pharmaOS ontology: node types (BLUEPRINT, STEP, SECTION, FIELD, FRAGMENT, INSTRUCTION), edge types, property constraints, and index strategy
- Build equipment dependency graph in the graph DB: upstream/downstream equipment relationships for impact analysis during CMMS work orders and deviation investigations
- Define the AI model provenance graph: model version nodes → training dataset edges → inference event edges, queryable for GxP AI audit trail requirements
- Implement vector search SLA monitoring: p95 ANN query latency targets per collection size, alerting on index drift requiring rebuild
Analytics & Data Warehouse Layer
- Design the Apache Iceberg table architecture on S3: partition spec (tenant_id + year + month), snapshot isolation for concurrent reads during batch close, time-travel for point-in-time regulatory queries
- Build dbt transformation DAG for analytical projections: batch genealogy, equipment utilization, deviation trend, CAPA effectiveness KPI tables
- Own Redshift Spectrum or Athena query optimization: partition pruning, columnar compression codec selection (Parquet + Snappy vs. ORC + ZSTD), workgroup cost controls per tenant
- Implement data lineage tracking for ALCOA+ analytics: every analytical record traces back to its source GxP event via immutable lineage metadata
- Define the Kafka → Iceberg streaming materialization path using Apache Flink or Spark Structured Streaming for near-real-time analytical freshness
AI Platform Database Support
- Design the AI model registry schema: model metadata, hyperparameters, validation status, deployment targets, drift metric history — all GxP-validated as Category 4 software data
- Own the prompt/response audit log persistence: every LLM interaction for GxP workflows stored with user identity, timestamp, model version, input hash, output hash for Part 11 audit trail
- Build the feature store backing tables in TimescaleDB: materialized feature vectors for ML inference, versioned by time window and model version
- Design database-layer support for HITL (human-in-the-loop) AI workflows: pending review queues, approval state machines, override logging — all immutable and auditable
Database Operations & SRE
- Define and enforce RPO < 1 minute / RTO < 15 minutes SLAs for all GxP-tier databases via continuous WAL archiving, Aurora Global Database, and automated restore testing
- Own database observability: pg_stat_statements, auto_explain, pgBadger analysis, custom Prometheus exporters, Grafana dashboards for query performance, lock waits, replication lag, autovacuum health
- Build automated slow query alerting pipeline: queries exceeding configurable thresholds trigger Jira tickets with EXPLAIN ANALYZE output attached
- Design and execute quarterly chaos drills: Aurora failover, replica promotion, cross-region switchover — all with GxP deviation report if RTO SLA is breached
- Own database cost governance: Aurora capacity unit analysis, reserved instance coverage, Iceberg storage cost per tenant chargeback model
WHAT YOU WILL BRING
Experience
- ·12+ years in database engineering; 5+ years as a principal/staff architect
- Deep PostgreSQL internals mastery: MVCC, WAL, autovacuum, planner statistics, extensions
- Aurora PostgreSQL: cluster topology, Global Database, Serverless v2, Parameter Groups
- Sharding design: Citus, logical replication, application-level sharding, RLS at scale
- TimescaleDB: hypertables, continuous aggregates, compression, chunk lifecycle management
- Kafka integration: producer/consumer patterns, topic design, at-least-once vs. exactly-once
- Cloud data platforms: Iceberg, Athena/Redshift Spectrum, dbt at production scale
- Migration tooling: Flyway or Liquibase; zero-downtime DDL in high-traffic systems
- Vector databases: pgvector, Qdrant, or Pinecone — ANN index tuning
- On-premises PostgreSQL HA: Patroni, repmgr, pgBackRest, etcd
- IaC for databases: Terraform RDS modules, Helm charts for DB operators
- Strong SQL and PL/pgSQL; Python or Go for automation scripts
Why Join Us (vs. a Big Company)??
True Ownership - You'll help design and build a platform from the group up. Every feature, every decision, every improvement - you'll be part of it.
Mission-Driven Work – We’re solving critical problems in pharma manufacturing that improve safety, compliance, and efficiency.
· Accelerated Growth– With us, you’ll learn faster, stretch your skills, and take on challenges that would take years to access elsewhere.
· Startup Energy, Real Impact– No red tape. No silos. Just smart, motivated people building something meaningful together.
· End-to-End Visibility– Be part of the full journey — from whiteboard to deployment — and actually see how your work changes the game.
- Build category-defining productsfor regulated life sciences
- Work on cutting-edge AI + vector searchin real-world GxP environments
- High ownership, real impact, and direct collaboration with US leadership
- Opportunity to influence platform architecture from early stages
Why You’ll Love It Here??
Here are some things you will be a part of:
· Build foundational products with real-world impact in life sciences.
· Work directly with founders and be part of early product leadership.
· Flexible remote work, async-friendly culture.
· Competitive equity, startup perks, and growth opportunities.
· Help define the culture of a product-first, purpose-driven startup.
Job Type: Full-time
Benefits:
- Cell phone reimbursement
- Health insurance
- Internet reimbursement
- Paid sick time
- Paid time off
- Work from home
Application Question(s):
- Are you currently an AI Engineer?
Work Location: Remote
