Remote Data Engineer — Databricks, AWS, Pipelines (PST)

Octagon IT; USA; Remote; USD 110.000 - 160.000; Vollzeit

Zusammenfassung

Octagon IT is seeking an experienced Data Engineer to design, build, and scale robust data pipelines, data warehouses, and automated ETL workflows. You will leverage SQL, Python, Databricks, and AWS to deliver reliable, high-throughput data analytics across mission-critical platforms.

You’ll own data pipeline architecture, ingestion frameworks, and API-driven data access, collaborating with cross-functional teams in an agile environment.

Qualifikationen

• Bachelor’s degree in Computer Science, Data Science, Software Engineering, Information Technology, or a related technical discipline.
• Minimum of 3 to 6 years of progressive professional experience in data engineering, big data analytics, and ETL development.
• Strong, demonstrable hands‑on professional expertise in advanced SQL programming and data modeling.
• Comprehensive working knowledge and practical proficiency in Python scripting for data engineering and automation.

Aufgaben

• Design, develop, test, optimize, and maintain scalable data pipelines and automated ETL/ELT integration workflows.
• Build and maintain high-performance big data processing pipelines and analytics notebooks utilizing Databricks.
• Author complex, highly optimized SQL queries, stored procedures, and data transformation scripts.
• Develop robust data automation and pipeline orchestration scripts utilizing Python programming.

Kenntnisse

SQL

Python

Databricks

Spark

AWS

ETL

Data Modeling

English (written & spoken)

Ausbildung

Bachelor’s degree in Computer Science or related field

Tools

Databricks

Apache Spark

AWS

Airflow

dbt

Jobbeschreibung

Octagon IT is seeking an experienced Data Engineer to design, build, and scale robust data pipelines, data warehouses, and automated ETL workflows. You will leverage SQL, Python, Databricks, and AWS to deliver reliable, high-throughput data analytics across mission-critical platforms.

You’ll own data pipeline architecture, ingestion frameworks, and API-driven data access, collaborating with cross-functional teams in an agile environment.