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Data Engineer (Databricks)

Posted 2024-10-25

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💎 Seniority level: Senior, 3+ years of industry experience in Data Engineering, including expertise in Spark and SQL.

📍 Location: Canada, United States

💸 Salary: 130000 - 160000 CAD per year

🔍 Industry: Performance marketing

🏢 Company: Fluent, LLC

🗣️ Languages: English

⏳ Experience: 3+ years of industry experience in Data Engineering, including expertise in Spark and SQL.

🪄 Skills: SQLData engineeringSparkCollaboration

Requirements:
  • Bachelor's or Master's degree in computer science.
  • 3+ years of industry experience in Data Engineering.
  • Expertise in Spark and SQL.
  • 1+ years of experience with Databricks environment.
  • Familiarity with real-time ML systems within Databricks is beneficial.
Responsibilities:
  • Execute on building data products to power Fluent’s business lines.
  • Develop, deploy, and manage scalable pipelines on Databricks.
  • Ensure robust integration with a Feature Store leveraging online tables for ML models.
  • Investigate and leverage Databricks’ capabilities for real-time data processing.
  • Maintain high quality of code with comprehensive data observability.
  • Partner with data science, UI, and reporting teams to understand data requirements.
  • Share knowledge by giving tech talks and evangelizing best practices.
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🔥 Sr. Data Engineer (Databricks)
Posted 2024-10-25

📍 Canada, United States

🧭 Full-Time

💸 160000 - 180000 CAD per year

🔍 Performance marketing

🏢 Company: Fluent, LLC

  • Bachelor's or Master's degree in computer science.
  • 5+ years of industry experience in Data Engineering.
  • Expertise in Spark and SQL.
  • 2+ years of experience with the Databricks environment.
  • Familiarity with real-time ML systems within Databricks is beneficial.

  • Majority of the role will be software engineering – creating tables, views, Spark jobs, orchestrating within the Databricks environment.
  • Develop, deploy, and manage scalable data pipelines on Databricks with robust integration for machine learning models.
  • Implement real-time data processing and streaming using Databricks capabilities.
  • Maintain high code quality with observability, metadata standards, and best practices.
  • Work with data science, UI, and reporting teams to translate data requirements into models.
  • Stay updated on emerging tech trends within the Databricks ecosystem.
  • Share knowledge through events and empower internal teams with architectural insights.

SQLData engineeringSparkCollaboration

Posted 2024-10-25
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