Key skills
AWS RedshiftETL/ELT frameworksData pipeline designSQL proficiencyAWS data platformsData modelingDistributed systemsCommunication skillsCollaboration skillsProblem-solving skillsPythonJavaScalaSQLMachine LearningMLOpsETLELTData EngineeringData WarehousingAnalyticsSnowflakeRedshiftDatabricksSparkKafkaAirflowdbtAWSTerraformCloudFormationLambdaS3GlueKinesisCI/CDCommunication
About this role
Sezzle is a fintech company on a mission to financially empower the next generation by revolutionizing the shopping experience. They are seeking a Principal Data Engineer to take ownership of their data warehousing infrastructure and optimize data pipelines for analytics and operational workflows.
Responsibilities:
- 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
Requirements:
- 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 machine learning pipelines, feature stores, or MLOps concepts
- Experience leading data platform migrations, warehouse re-architectures, or large-scale performance overhauls
- Enthusiasm for automation, CI/CD for data, and infrastructure as code (Terraform, CloudFormation)