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