End to end ownership of our database and data warehousing infrastructure, its KPIs and its SLAs, including MySQL, Postgres, CDC capture, and Redshift.
Lead optimizations in database and query performance.
Build and maintain tooling to help manage and upgrade our data infrastructure, continually improving with migrations and optimizations.
Partner with Development to ensure we ship solid queries against a battle-tested schema to meet the needs of our products and business.
Help Sezzle evolve our data systems beyond the current scale and stack with whatever tooling is necessary to handle processing high volumes of writes and events while performing complex ETL.
Evaluate and integrate new technologies, guiding the evolution of Sezzle’s data infrastructure.
Help data engineers and analysts optimize Redshift and warehouse performance, including query tuning, modeling improvements, and cost management.
What We look for:
12+ years of experience in DBA, SRE, or Data Engineering roles, with a strong track record of scaling production-grade systems.
Deep expertise with MySQL, Postgres, AWS Redshift or similar products, including performance tuning, table design, and workload management.
Advanced proficiency in SQL.
Strong hands-on experience with data replication and ETL/ELT frameworks, especially DBT, AWS DMS, or similar tools.
Strong understanding of data modeling, distributed systems, and warehouse/lake design patterns.
Ability to work in a fast-paced, collaborative environment with excellent communication and documentation skills.
Demonstrated experience working with Claude or equivalent large language model tools is required; candidates must be comfortable leveraging AI to enhance productivity, research, and communication.
Preferred Knowledge and Skills:
Prior experience in high-growth, data-intensive fintech or similar regulated environments.
Knowledge of lakehouse architectures and modern stacks such as Snowflake, Databricks, Iceberg, or Delta Lake.
Some background in designing scalable, fault-tolerant data pipelines using modern orchestration tools (Airflow, Dagster, Prefect, etc.) processing anywhere from 100GB to 1 TB of new data a day
Familiarity with streaming technologies (Kafka, Kinesis, Flink, Spark Streaming).