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Director, Data Engineering

Posted 2024-08-07

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💎 Seniority level: Senior, 12+ years

📍 Location: United States

💸 Salary: $248,000 - $280,000 per year

🔍 Industry: Sports gaming

🏢 Company: Underdog Sports

🗣️ Languages: English

⏳ Experience: 12+ years

🪄 Skills: AWSLeadershipPythonSQLBusiness IntelligenceGCPJavaKafkaMachine LearningAzureData engineeringData scienceCommunication Skills

Requirements:
  • At least 12+ years of experience in data engineering, data warehousing, and/or business intelligence roles.
  • Experience scaling and managing 10+ person teams.
  • Strong familiarity with distributed computing and data storage mechanisms on the cloud environment (e.g. AWS/GCP/Azure) with hands-on experience in managing data infrastructure.
  • Track record of delivering scalable and innovative solutions, leveraging cutting-edge technologies for data warehousing and machine learning such as Kafka, DBT, Databricks.
  • Excellent leadership and communication skills with ability to influence and collaborate with stakeholders.
  • Advanced degree in Computer Science, Data Science, or a related field.
  • Advanced proficiency with Python, Java, or other OOP languages (at least 2).
  • Advanced proficiency with SQL.
Responsibilities:
  • As a Director of Data Engineering, you’ll be responsible for roadmapping and implementing a vision for a scalable analytics data platform.
  • Establish best practices that achieve operational excellence in data availability, data quality, and data privacy.
  • Collaborate with the data science and data analytics teams to deliver high-impact data assets.
  • Design and implement data pipelines and transformation workflows that are optimized for flexibility and data quality, leveraging best-in-class tools for transformation.
  • Lead, mentor, and scale the data engineering team, cultivating a culture of innovation, learning, and excellence.
  • Partner with product, engineering, and analytics teams to understand their data needs and translate their business requirements to scalable technical solutions.
  • Stay on top of industry trends, emerging technologies, and best practices for data processing, storage, compute and ML development.
  • Develop and maintain robust governance frameworks, ensuring processes for monitoring, observability, and testing are in place to safeguard data integrity and availability.
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