Sr. Database Architect

As a Senior Database Engineer, you will drive data infrastructure that enables data-informed decision-making, applying modern engineering and distributed systems practices. You will partner closely with Product Managers, Data Analysts, Software Engineers, and business stakeholders to deliver stable, high-quality data pipelines, enterprise reporting, and datasets that support analytics and machine learning use cases.

A primary responsibility of this role is to optimize and maintain our OLTP databases, ensuring reliability, performance, and production readiness. This includes auditing systems, improving data quality, refactoring legacy pipelines, and applying best practices for schema design, indexing, and query performance. In parallel, you will build and maintain scalable data systems that power advanced analytics across the business.

You will design performance testing and release validation frameworks to prevent regressions and ensure data integrity, while establishing strong production processes such as monitoring, alerting, backup and recovery, access controls, and incident response.

• Bachelor's degree in Mathematics, Statistics, Computer Science, or related field

• 5+ years of experience as a Database Engineer, Data Engineer, or similar role

• Experience designing, implementing, and maintaining high performant, scalable OLTP systems.

• Hands-on experience and advanced knowledge of SQL (e.g., Postgres, Snowflake)

• Strong experience with data modeling, data warehouses, and lakehouse architectures

• Experience designing and implementing scalable data architectures, including batch and streaming pipelines

• Experience building ELT pipelines with dbt and Snowflake

• Intermediate to advanced Python development skills

• Experience assessing and improving existing database systems, including performance tuning (indexing, query optimization, partitioning) and data quality remediation

• Strong understanding of database internals and transactional systems

• Experience implementing backup, recovery, and high-availability strategies

• Experience designing and implementing performance/load testing frameworks for data systems

• Knowledge of benchmarking, regression testing, and release validation processes

• Experience building automated testing pipelines to ensure data quality and system performance across deployments

• Experience defining and maintaining production database processes, including monitoring, alerting, and incident response

• Familiarity with observability tools and practices (logging, metrics, tracing)

• Strong understanding of SLAs, SLOs, and data reliability best practices

• Experience with AWS data technologies (Glue, Kinesis, Lambda)

• Experience with orchestration tools (Airflow)

• Experience with infrastructure-as-code (Terraform)

• Knowledge of the Software Development Lifecycle