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Senior Data Engineer - Platform Engineering

Posted 2024-10-11

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💎 Seniority level: Senior, 8+ years

📍 Location: United States

💸 Salary: $185,440 - $231,800 per year

🔍 Industry: Entertainment, Streaming Services, Anime

🏢 Company: Crunchyroll, LLC

🗣️ Languages: English

⏳ Experience: 8+ years

🪄 Skills: AWSPythonSQLDynamoDBJavaKafkaSnowflakeApache KafkaData engineeringNosqlCommunication SkillsCI/CD

Requirements:
  • Bachelor's degree in Computer Science, Information Technology, or a related field.
  • 8+ years of experience in data engineering, focused on building and optimizing data pipelines, data architecture, and eventing systems.
  • Extensive experience with AWS cloud platform and their data-related services.
  • Proficiency in automation frameworks (e.g., Terraform, Cloud Formation).
  • Proficiency in data lake and pipeline tools and frameworks (e.g., Databricks, Apache Kafka/Kinesis, AWS Glue).
  • Proficiency in one or more programming languages (e.g., Python, Java).
  • Strong understanding of SQL (e.g., Redshift, Snowflake, RDS) and NoSQL databases (e.g., DynamoDB).
  • Experience with schema design and query tuning for search optimization.
  • Experience in building CI/CD pipelines, testing frameworks (e.g., dbt).
  • Strong problem-solving skills and a proactive approach to identifying and addressing issues.
  • Ability to independently own and execute projects and collaborate effectively.
  • Strong communication skills and the ability to mentor and guide junior engineers.
Responsibilities:
  • Crunchyroll is growing and changing, presenting unique challenges and opportunities to support millions of anime fans around the world.
  • As a Senior Data Engineer, you will play a pivotal role in designing, implementing, and optimizing data services and pipelines.
  • Ensure that the data services support the needs of the business.
  • Collaborate with cross-functional teams to enable operational data analysis, enhance eventing systems, and develop tools.
  • Your expertise will drive 100% automation, best practices, create scalable architectures, and optimize data environments.
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