Remote Cloud Data Engineer (GCP) — ETL & Data Modeling

Satalia; United States; Remote; USD 100,000 - 133,000; Full time

Job summary

Satalia is seeking a data engineer to build and maintain the cloud data foundations behind analytics and AI systems. You’ll own pipelines and models that convert raw information into dependable, documented datasets, collaborating with data scientists and software engineers from design through production.

You will develop ETL/ELT workflows, create SQL transformations, design data warehouses and lakehouses, and prepare metadata so AI agents can query data safely.

Qualifications

• Experience building production data pipelines and data models.
• Strong SQL and Python with version control, testing, and CI/CD.
• Experience with Airflow or Dagster.
• GCP BigQuery/Pub/Sub/IAM or AWS equivalents; Terraform.
• Exposure to LLM/agent-based systems and embeddings.
• Ability to communicate technical designs to product and engineering.

Responsibilities

• Develop and operate ETL/ELT pipelines that ingest, transform, and deliver large datasets.
• Create tested, version-controlled SQL transformations and data models for analytics, BI, and AI use cases.
• Design warehouse, lakehouse, and relational data structures to suit different access patterns.
• Prepare metadata, catalogues, and embeddings so AI agents can discover and query data safely.
• Set up orchestration, monitoring, quality checks, and lineage to improve data reliability.
• Collaborate across engineering and data teams, and document technical decisions clearly.

Skills

SQL

Python

Version control

Testing

CI/CD

Tools

Airflow

Dagster

Terraform

BigQuery

Pub/Sub

IAM

dbt

Spark

Docker

Job description

Satalia is seeking a data engineer to build and maintain the cloud data foundations behind analytics and AI systems. You’ll own pipelines and models that convert raw information into dependable, documented datasets, collaborating with data scientists and software engineers from design through production.

You will develop ETL/ELT workflows, create SQL transformations, design data warehouses and lakehouses, and prepare metadata so AI agents can query data safely.