Senior Data Engineer (Azure) – Remote/USA

Remote / Distributed Team | $45/hr | Azure Data Platform | Databricks | PySpark | Enterprise Data Engineering

Job Overview

A company is hiring a Senior Data Engineer to join a distributed global team responsible for building and supporting enterprise-scale data platforms.

This is a strong opportunity for experienced data engineers who specialize in the Azure ecosystem and have deep hands-on expertise in:

• Azure Databricks
• PySpark
• Azure Data Factory
• Data warehousing
• SQL
• Python
• Enterprise data modeling

If you enjoy designing scalable cloud data systems, building high-performance pipelines, and supporting analytics or AI/ML-ready data platforms, this role looks like a very strong fit.

Job Summary

This role focuses on building and maintaining scalable, reliable, enterprise-grade data solutions inside the Azure cloud environment.

You’ll work closely with both onshore and offshore teams, meaning this is a highly collaborative position in a globally distributed engineering setup.

The company is specifically looking for someone who can:

• Design robust data models
• Build and optimize large-scale data pipelines
• Support enterprise reporting and analytics
• Maintain strong data quality and platform reliability
• Help prepare data for AI/ML workflows

This is not an entry-level cloud data role. It is clearly aimed at a senior-level engineer who can own core parts of a modern Azure data platform.

Key Responsibilities

As a Senior Data Engineer, your responsibilities will include:

• Collaborating with globally distributed engineering teams
• Working with both onshore and offshore teams to deliver reliable data solutions
• Designing and implementing enterprise-grade data models
• Supporting data structures for:
  • Analytics
  • Reporting
  • Business intelligence
  • Advanced data use cases
• Building, optimizing, and maintaining data pipelines using:
  • Azure Databricks
  • PySpark
  • Azure Data Factory
• Developing and improving data warehouse architectures
• Supporting enterprise BI and analytics workloads
• Ensuring high standards for:
  • Data quality
  • Data integrity
  • Platform performance
  • Reliability
• Supporting:
  • Data ingestion
  • Data transformation
  • Data preparation
• Helping prepare datasets for AI/ML use cases
• Participating in Agile / Scrum ceremonies
• Contributing to continuous improvement initiatives

This role combines hands-on engineering, platform design, and cross-team collaboration.

Core Technical Stack

The role is strongly centered on the Azure data ecosystem.

Primary technologies include:

• Azure Databricks
• PySpark
• Azure Data Factory (ADF)
• Python
• SQL
• Azure cloud data services
• Enterprise data warehousing
• Data modeling

Nice-to-have / bonus technologies:

• Apache Airflow (or similar orchestration tools)
• Exposure to AI/ML data pipelines

This suggests the company likely wants someone who can work across both modern lakehouse-style processing and traditional enterprise warehouse design.

Required Experience

The posting is very clear about the expected experience level.

Required experience includes:

• Python: 5+ years of hands-on development experience
• SQL: 6–8 years of experience writing complex queries and optimizing performance
• Data Modeling: 5+ years designing and implementing enterprise-level data models
• Strong experience with Azure cloud data services
• Hands-on experience with Azure Databricks
• PySpark: 5–7 years building scalable data processing and transformation pipelines
• Experience developing and orchestrating workflows using Azure Data Factory

This is a very senior profile.

If your background is mostly limited to basic ETL or low-code tools without deep engineering in Databricks / PySpark / SQL optimization, this role may be too advanced.

Preferred Qualifications

The company would especially value candidates who also bring:

• Experience with Apache Airflow or similar orchestration tools
• Exposure to or experience supporting AI/ML data pipelines
• Experience working in Agile / Scrum environments
• Proven ability to collaborate effectively with globally distributed teams

This is important because it shows the company is likely thinking beyond traditional reporting pipelines and moving toward data platform maturity, including support for machine learning and advanced analytics.

What This Role Really Involves

At a practical level, this role is about owning major parts of an enterprise Azure data platform.

That means you are likely expected to be strong in all of the following:

• Data ingestion architecture
• Batch / large-scale transformation
• Distributed compute using Spark
• Warehouse and model design
• Performance tuning
• Workflow orchestration
• Data reliability and governance support
• Collaboration across time zones and teams

This is the kind of role where companies expect you to think not just as a coder, but as a platform engineer + architect-minded senior builder.

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Best Fit Candidate Profile

This role is ideal for:

• Senior Data Engineers
• Azure Data Engineers
• Databricks Engineers
• Cloud Data Platform Engineers
• Big Data Engineers
• Data Warehouse Engineers
• Analytics Platform Engineers

It is especially strong for candidates who already have experience with:

• Large-scale Azure data platforms
• Databricks + Spark production pipelines
• Enterprise analytics ecosystems
• Data model design for BI and reporting
• Global engineering team collaboration

How to apply?

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