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

Headquarters: Remote
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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.

• 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

• 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

• 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

• 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