Senior Data Engineer — Azure, Databricks & ML Pipelines | Remote
Vollständige Stellenanzeige von PVH (Tommy Hilfiger/Calvin Klein)
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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
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Design, build, and maintain data pipelines using Databricks and Azure-native data services
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Develop and optimize ETL/ELT processes to support analytics and machine learning workloads
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Build and maintain CI/CD pipelines for data engineering and ML deployment workflows
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Write clean, efficient, production-quality Python for data processing and pipeline automation
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Support machine learning teams with well-structured, high-quality datasets and feature pipelines
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Design and manage data architecture across Azure services (e.g., Azure Data Factory, Azure Data Lake, Azure Synapse)
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Monitor pipeline performance, troubleshoot data quality issues, and implement reliability improvements
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Implement data governance, security, and access control best practices
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Collaborate with data scientists, analysts, and software engineers to align data infrastructure with business needs
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Participate in code reviews, architecture discussions, and technical planning
What You Bring
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Strong hands-on experience with Azure cloud data services
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Proven experience building and maintaining pipelines on Databricks
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Solid experience designing and managing CI/CD pipelines for data or ML workflows
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Strong Python skills for data engineering and pipeline development
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Working knowledge of machine learning workflows and how data engineering supports them
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Experience with SQL and relational/distributed data systems
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Understanding of data pipeline orchestration, monitoring, and reliability practices
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Strong problem-solving skills and ability to work independently on complex data infrastructure challenges
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Solid communication skills for collaborating with data science and engineering teams
Nice to Have
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Experience with MLOps practices and tools (MLflow, Azure ML)
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Familiarity with Spark internals and performance tuning within Databricks
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Experience with infrastructure-as-code (Terraform, Bicep, ARM templates)
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Exposure to real-time/streaming data pipelines (Kafka, Event Hubs, Structured Streaming)
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Relevant Azure or Databricks certifications
Why This Role
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Full pipeline ownership: Own data infrastructure end to end, from ingestion through ML-ready delivery
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Modern data stack: Work with Azure and Databricks, leading platforms in enterprise data engineering
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Cross-functional impact: Directly enable machine learning and analytics outcomes, not just move data
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Flexibility: Remote-friendly engagement structure
Bereit?
Bewerbung für PVH (Tommy Hilfiger/Calvin Klein) fortsetzen · kein Konto nötig.