As the Sr. Data Engineer - Architecture, 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.

What You Need (Required Knowledge, Skills & Abilities):

Education & Experience

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

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

Core Data Engineering & Architecture

• 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

Database Optimization & Reliability

• 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