Key skills
AirflowAmazon RedshiftApacheAWSBigQueryPostgresPythonSparkAIData EngineeringSnowflakeRedshiftApache SparkApache AirflowGitHub ActionsLambdaGluePostgreSQLGitGitHubVersion ControlCI/CD
About this role
Role Overview
- Build data ingestion pipelines integrating AI tools and internal platforms into Snowflake
- Maintain and harden the existing Snowflake infrastructure — schemas and tables that grew organically without data engineering input — and bring them up to standard
- Deploy work through CI/CD pipelines into Airflow or AWS Glue
- Manage and process access requests
- Collaborate proactively with product managers and engineers to identify data needs
Requirements
- 5+ years of data engineering experience
- Python for scripting, API development, and pipeline creation
- Apache Airflow — for pipeline orchestration; Dagster or Prefect accepted as alternatives
- AWS services — especially Glue, Lambda; experience deploying and maintaining production workloads
- Apache Spark — for distributed processing, particularly within AWS Glue
- Snowflake — preferred data warehouse; Redshift or BigQuery accepted if concepts transfer cleanly
- CI/CD pipelines — GitHub Actions or similar; this is how pipelines and scripts are deployed to Airflow and Glue
- API experience — consuming third-party APIs and building internal APIs with Python)
- Git / GitHub — version control, branching strategy, pull request workflow
- PostgreSQL or other OLTP databases — for operational data access and integration.
Tech Stack
- Airflow
- Amazon Redshift
- Apache
- AWS
- BigQuery
- Postgres
- Python
- Spark
Benefits
- Fully Remote
- Health insurance (specifics not mentioned)
- Professional development opportunities