Principal Engineer - Data Infrastructure , Remote

United States, Remote

The salary range for this role is negotiable, the range being $200,000 - $300,000 per year.

With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting‑edge tech with seamless, interest‑free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values.

As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game‑changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping!

We are seeking a talented and motivated, best‑in‑class Principal Data Engineer. This role presents an exciting opportunity to thrive in a dynamic, fast‑paced environment within a rapidly growing team, with abundant prospects for career advancement.

Sezzle is growing; our data generation and consumption is growing with us at an increasing scale. Data is extremely valuable, and we want to empower the business, the engineers, and the rest of the organization to be able to analyze large volumes of it quickly and efficiently.

Sezzle is a heavy consumer of Redshift, leveraging AWS DMS, DBT‑based transformations, and similar tooling to populate a rapidly growing data lake that supports multiple data warehouses for other business units. It’s scaled well with us so far, and we’re looking to continue improving it, but we also want to step into new tooling and technologies.

• End to end ownership of our data warehousing infrastructure, its KPIs and its SLAs.
• Design, build, and optimize large‑scale, high‑performance data pipelines to support analytics, product insights, and operational workflows.
• Architect and evolve Sezzle’s data ecosystem, driving improvements in reliability, scalability, and efficiency.
• Help Sezzle evolve our data systems beyond the current scale and stack with whatever tooling is necessary to handle processing high volumes of events and performing complex ETL.
• Lead development of ETL/ELT workflows on our current tooling using Redshift, DBT, AWS DMS, and related modern data tooling.
• Partner with cross‑functional teams (engineering, analytics, product, finance, risk) to gather or adapt requirements and deliver robust, high‑quality datasets.
• Evaluate and integrate new technologies, guiding the evolution of Sezzle’s data stack and infrastructure.
• Optimize Redshift and warehouse performance, including query tuning, modeling improvements, and cost management.

• 12+ years of experience in data engineering, with a strong track record of building and scaling production‑grade systems.
• Deep expertise with AWS Redshift or similar products
  , including performance tuning, table design, and workload management.
• Strong hands‑on experience with ETL/ELT frameworks
  , especially DBT
  , AWS DMS
  , or similar tools.
• Proficiency in SQL (advanced level) and at least one programming language such as Python
  , Scala
  , or Java
  .
• Experience building and maintaining AWS‑based data platforms
  , including S3, Lambda, Glue, or EMR.
• Track record designing scalable, fault‑tolerant data pipelines using modern orchestration tools (Airflow, Dagster, Prefect, etc.) processing more than 100GB – 1 TB of new data a day.
• 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.

• Prior experience in high‑growth, data‑intensive fintech or similar regulated environments.
• Familiarity with streaming technologies (Kafka, Kinesis, Flink, Spark Streaming).
• Knowledge of lakehouse architectures and modern stacks such as Snowflake, Databricks, Iceberg, or Delta Lake.
• Exposure to machin…