Data Engineer

Must-haves 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 Nice-to-haves 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 What you will work on 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