Python Data Platform Engineer - Scalable Pipelines

Recru, LLC.; Houston (TX); On-site; USD 90,000 - 130,000; Full time

Job summary

Recru, LLC is seeking a Python Software Engineer with Data Engineering experience to join a global team building a large-scale Metering Data Platform. The role focuses on production-grade applications and scalable data pipelines rather than notebook-style ETL.

You will develop Python-based services, PySpark/Databricks pipelines, REST APIs, and CI/CD tooling, collaborating with international teams to centralize customer usage data in a data lake and shape platform architecture.

Qualifications

• Strong Python software engineering background; Python developer first, data engineer second.
• Proficient in Python, Django and/or Flask, PySpark, Databricks, REST APIs, object‑oriented design, and Git.
• Experienced in building data pipelines and data lakes.
• Skilled at Spark optimization and performance tuning.
• Knowledgeable in data modeling and distributed systems.

Responsibilities

• Build and maintain Python‑based applications, APIs, and data services.
• Develop scalable data ingestion and processing solutions using PySpark and Databricks.
• Create reusable frameworks, CI/CD processes, monitoring, and testing capabilities.
• Collaborate with global teams to build a shared platform that aggregates customer usage data into a centralized data lake.
• Contribute to architecture decisions, engineering standards, and platform growth.

Skills

Python

Django/Flask

PySpark

Databricks

REST APIs

Git

Object-oriented design

Data pipelines

Data lakes

Spark optimization

Distributed systems

Job description

Recru, LLC is seeking a Python Software Engineer with Data Engineering experience to join a global team building a large-scale Metering Data Platform. The role focuses on production-grade applications and scalable data pipelines rather than notebook-style ETL.

You will develop Python-based services, PySpark/Databricks pipelines, REST APIs, and CI/CD tooling, collaborating with international teams to centralize customer usage data in a data lake and shape platform architecture.