Build and maintain scalable data solutions and data products using a modern Lakehouse architecture deployed on AWS and Google Cloud Platform (GCP)
Job Summary
Build and maintain scalable data solutions and data products using a modern Lakehouse architecture deployed on AWS and Google Cloud Platform (GCP).
Implement reliable data pipelines, curated datasets, and domain-aligned data products that support analytics, reporting, and downstream AI/ML use cases.
Carrier is committed to offering competitive benefits programs, including a retirement savings plan, health insurance, flexible schedules, parental leave, and professional development opportunities.
Matching Summary
Build and maintain scalable data solutions and data products using a modern Lakehouse architecture deployed on AWS and Google Cloud Platform (GCP).
Skills & Requirements
Must-have
Lakehouse architecture on AWS and GCP
Build and maintain data pipelines
Apache Iceberg, AWS Glue, Spark, Kinesis, Athena
GCP Dataproc, Dataflow, Pub/Sub, BigQuery
Medallion architecture patterns
Python and SQL proficiency
Nice-to-have
Multi-cloud data environments
Orchestration tools like Airflow
CI/CD and Infrastructure as Code
Supporting AI/ML workloads
Data product-oriented team experience
Key Requirements
2-4 years of experience in data engineering
Hands-on experience building data pipelines on cloud platforms
Experience with AWS data services
Experience with GCP data services
Familiarity with Lakehouse concepts and open table formats
Understanding of batch and streaming data processing