Senior Data Engineer — Azure, Databricks & ML Pipelines | Remote

About this role

Headquarters: Remote

URL: https://www.toptal.com/

About the Role

We're looking for a Senior Data Engineer to design, build, and maintain scalable data pipelines and ML-ready infrastructure on Azure and Databricks. This is a hands-on engineering role: you'll own the full data pipeline lifecycle — ingestion, transformation, orchestration, and deployment — while supporting machine learning workflows with clean, reliable data. If you're comfortable owning infrastructure decisions and writing production-quality Python at scale, this role is built for that.

What You'll Do

Design, build, and maintain data pipelines using Databricks and Azure-native data services

Develop and optimize ETL/ELT processes to support analytics and machine learning workloads

Build and maintain CI/CD pipelines for data engineering and ML deployment workflows

Write clean, efficient, production-quality Python for data processing and pipeline automation

Support machine learning teams with well-structured, high-quality datasets and feature pipelines

Design and manage data architecture across Azure services (e.g., Azure Data Factory, Azure Data Lake, Azure Synapse)

Monitor pipeline performance, troubleshoot data quality issues, and implement reliability improvements

Implement data governance, security, and access control best practices

Collaborate with data scientists, analysts, and software engineers to align data infrastructure with business needs

Participate in code reviews, architecture discussions, and technical planning

What You Bring

Strong hands-on experience with Azure cloud data services

Proven experience building and maintaining pipelines on Databricks

Solid experience designing and managing CI/CD pipelines for data or ML workflows

Strong Python skills for data engineering and pipeline development

Working knowledge of machine learning workflows and how data engineering supports them

Experience with SQL and relational/distributed data systems

Understanding of data pipeline orchestration, monitoring, and reliability practices

Strong problem-solving skills and ability to work independently on complex data infrastructure challenges

Solid communication skills for collaborating with data science and engineering teams

Nice to Have

Experience with MLOps practices and tools (MLflow, Azure ML)

Familiarity with Spark internals and performance tuning within Databricks

Experience with infrastructure-as-code (Terraform, Bicep, ARM templates)

Exposure to real-time/streaming data pipelines (Kafka, Event Hubs, Structured Streaming)

Relevant Azure or Databricks certifications

Why This Role

Full pipeline ownership: Own data infrastructure end to end, from ingestion through ML-ready delivery

Modern data stack: Work with Azure and Databricks, leading platforms in enterprise data engineering

Cross-functional impact: Directly enable machine learning and analytics outcomes, not just move data

Flexibility: Remote-friendly engagement structure

How to Apply

Ready to bring your data engineering expertise to Azure and Databricks-powered ML infrastructure? Apply through Toptal here: https://www.toptal.com/talent/apply

To apply: https://weworkremotely.com/remote-jobs/toptal-senior-data-engineer-azure-databricks-ml-pipelines-remote