Contribute to data engineering initiatives within an experienced data organization
Explore and analyze connected datasets to identify patterns and develop high-quality data models
Partner with the data engineering group to refine and optimize transformation workflows
Design, build, and operationalize large-scale data solutions using AWS services and third-party technologies (Spark, EMR, DynamoDB, Redshift, Kinesis, Lambda, Glue, Snowflake)
Build production data pipelines covering ingestion through consumption using SQL and Python
Implement data engineering, ingestion, and curation functions on AWS using native or custom tooling
Lead proofs of concept and guide the transition of validated solutions into scalable production environments across engineering, deployment, and commercialization
Collaborate with analytics teams using Looker, QuickSight, and Q to provide clean and reliable datasets
2+ years of data engineering experience
Experience with Python for data engineering work involving ETL/ELT pipelines and related components
Proficiency with SQL, Python and other data-focused languages
Ability to design scalable solutions, evaluate emerging data technologies and anticipate new trends to address complex challenges
Strong communication skills in both spoken and written English
Startup experience
Familiarity with Snowflake
Familiarity with AWS
Experience with DBT, Dagster, Apache Iceberg or Infrastructure as Code
Knowledge of scalable data lake and streaming patterns
Bachelor’s Degree in Computer Engineering, Computer Science, or equivalent
