Principal Engineer (Data Infrastructure)

Role overview

Join Sezzle as a Principal Data Engineer (Data Infrastructure). You’ll own the end-to-end data warehousing infrastructure, architect the data ecosystem, design and optimize data pipelines, and guide the evolution of Sezzle’s data stack by evaluating and integrating new technologies.

Key responsibilities

• End-to-end ownership of data warehousing infrastructure: design, build, and optimize data pipelines.
• Lead ETL/ELT workflow development and collaboration with cross-functional teams to deliver high-quality datasets.
• Architect and evolve the data stack by evaluating and integrating new technologies.
• Design scalable, fault-tolerant pipelines and ensure defects are prevented from propagating downstream.
• Drive engineering excellence through automation, CI/CD for data, and infrastructure as code.

Requirements

• 12+ years of data engineering experience building and scaling production-grade systems.
• Experience with AWS-based data platforms, including S3 and at least one of: Lambda, Glue, EMR.
• Advanced SQL and at least one programming language: Python, Scala, or Java.
• Strong understanding of data modeling, distributed systems, and warehouse/lake design patterns.
• Track record building scalable, fault-tolerant pipelines using modern orchestration tools such as Airflow, Dagster, or Prefect, processing 100GB–1TB+ per day.
• Hands-on with ETL/ELT frameworks, especially dbt and/or AWS DMS (or similar).
• Deep expertise with AWS Redshift (or similar), including performance tuning, table design, and workload management.
• Familiarity with streaming technologies: Kafka, Kinesis, Flink, Spark Streaming.
• Strong communication and documentation skills; high standards and ability to challenge decisions.

Nice to have / additional signals

• Experience leading data platform migrations, warehouse re-architectures, or large-scale performance overhauls.
• Knowledge of lakehouse architectures and modern tooling such as Snowflake, Databricks, Iceberg, Delta Lake.
• Experience with Terraform and/or CloudFormation (infrastructure as code).
• Prior work in high-growth, data-intensive fintech or other regulated environments.
• Exposure to machine learning pipelines, feature stores, or MLOps concepts.