Job Title: Principal / Staff Database Architect

Location: Remote

Employment Type: Full-time

Role Type: Individual Contributor (IC)

About Aviso AIL:

Aviso AI is revolutionizing enterprise sales intelligence with cutting-edge AI solutions for forecasting, deal guidance, revenue operations, sales engagement, and conversational intelligence. Our AI-powered platform helps enterprise teams make smarter decisions, improve sales execution, and drive predictable revenue.

We are looking for a Principal / Staff Database Architect with 7+ years of experience who is deeply hands-on with modern database technologies and large-scale production systems. This is an architecture and engineering role—not a traditional DBA or data engineering position.

The ideal candidate will have strong expertise in MongoDB, along with solid experience in ClickHouse and PostgreSQL, and will be responsible for designing scalable database architectures, improving performance, optimizing infrastructure costs, and establishing database engineering standards across the platform.

Role Overview:

As a Principal / Staff Database Architect, you will work closely with application engineering, platform, and data engineering teams to design and evolve database architectures that support Aviso AI's high-scale, multi-tenant platform.

You will play a key role in solving complex database and API performance challenges, defining scaling strategies, improving reliability, and determining the right data store for different workloads.

This role will define database architecture and the contracts between database and data-platform layers, while the data engineering team retains ownership of pipeline implementation, transformations, orchestration, and analytical dataset delivery.

Key Responsibilities:

• Define and establish a consistent database architecture, indexing, and query optimization strategy across the platform.
• Diagnose and resolve database-related API latency and performance bottlenecks.
• Design and optimize MongoDB architectures for large-scale, multi-cluster, and multi-tenant environments.
• Define MongoDB sharding, replication, read/write scaling, indexing, and data placement strategies.
• Optimize complex MongoDB workloads, including queries, aggregation pipelines, indexes, and storage utilization.
• Design and optimize ClickHouse workloads, including partitioning, ordering keys, materialized views, ingestion performance, retention, and storage optimization.
• Review and optimize PostgreSQL schemas, indexes, query plans, connection management, and scaling strategies.
• Establish database capacity planning, observability, reliability, and disaster recovery practices.
• Evaluate and optimize database infrastructure and storage costs without compromising performance or reliability.
• Define appropriate data placement across MongoDB, ClickHouse, PostgreSQL, Databricks, and other analytical platforms based on workload requirements.
• Design and implement database migrations with minimal downtime and production risk.
• Review database schemas, tenant boundaries, retention, archival, and data lifecycle strategies.
• Establish database architecture standards, best practices, and engineering guidelines.
• Partner closely with application, platform, and data engineering teams to drive implementation of database architecture decisions.
• Lead technical initiatives to improve database reliability, scalability, performance, and cost efficiency.
• Mentor engineers and provide technical leadership on complex database architecture and performance challenges.
• Partner with data engineering teams to define data contracts and movement patterns between operational and analytical stores, including schema evolution, change data capture, deletion propagation, replay/backfill, reconciliation, and freshness requirements.
• Define source-of-truth boundaries and consistency expectations across MongoDB, ClickHouse, PostgreSQL, Databricks, and derived datasets.

Qualifications:

• 7+ years of experience in database engineering, database architecture, backend engineering, or a closely related field.
• Deep hands-on production experience with MongoDB, preferably at large-scale, multi-cluster, and multi-tenant environments.
• Strong expertise in: MongoDB indexing and query optimization, Aggregation pipelines, Sharding and replication, Read/write scaling, Performance troubleshooting, Capacity planning and storage optimization
• Strong production experience with ClickHouse, including: Partitioning, Ordering keys, Materialized views, Ingestion optimization, Retention and archival, Storage optimization
• Strong working knowledge of PostgreSQL, including schema design, indexing, query plans, connection management, and scaling.
• Proven experience diagnosing and resolving database and API performance bottlenecks in production environments.
• Experience designing and executing large-scale database migrations with minimal downtime.
• Strong understanding of database observability, reliability, capacity planning, and infrastructure cost optimization.
• Experience working with multi-tenant SaaS architectures and understanding tenant isolation and data placement.
• Strong understanding of how to select and architect databases for operational, transactional, analytical, and high-volume workloads.
• Strong communication and collaboration skills, with the ability to influence architecture decisions across engineering teams.

Good to Have

• Experience with Databricks or other modern analytical/data platforms.
• Experience working with high-volume SaaS, enterprise, or AI/ML platforms.
• Experience with cloud-native database deployments and distributed systems.
• Experience with database observability and monitoring tools.
• Experience leading database architecture initiatives across multiple engineering teams.
• Experience mentoring senior engineers and driving technical standards across an organization.
• Working knowledge of batch and streaming data patterns, including ETL/ELT, CDC, Kafka or similar messaging platforms, and their impact on database performance, consistency, and scalability.
• Familiarity with data orchestration and processing platforms such as Databricks, Spark, or Prefect—without requiring hands-on ownership of data pipelines.