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Data Platform Engineer

Posted 6 months agoViewed

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💎 Seniority level: Senior, 5+ years

📍 Location: Ukraine

🔍 Industry: Transportation

🏢 Company: Lyft👥 5001-10000💰 $400,000,000 Post-IPO Equity about 4 years ago🫂 Last layoff almost 2 years agoRide SharingTransportationAppsMobile AppsSoftware

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: AWSPythonSQLETLHadoopJavaKafkaKubernetesAirflowData engineeringSparkCollaborationDocumentationTroubleshooting

Requirements:
  • 5+ years of experience in software/data engineering, data architecture, or a related field.
  • Strong programming skills in at least one language: Java, Scala, Python, or Go.
  • Strong SQL and data modeling skills.
  • Hands-on experience with Apache Big Data frameworks such as Hadoop, Hive, Spark, Airflow, Iceberg, etc.
  • Proficiency in AWS cloud services.
  • Strong understanding of distributed systems, large-scale data processing, and data storage/retrieval.
  • Experience with data governance, security, and compliance will be a plus.
  • Familiarity with CI/CD and DevOps practices will be a plus.
  • Excellent problem-solving skills.
  • Ability to work independently or as part of a team.
  • Strong communication and collaboration skills.
Responsibilities:
  • Design, build, and maintain scalable and reliable data storage solutions to support diverse data processing needs.
  • Optimize and scale the platform to accommodate increasing data volumes and user requests.
  • Improve data storage, retrieval, query performance, and overall system performance.
  • Collaborate with data scientists, analysts, and other stakeholders to understand requirements and deliver tailored solutions.
  • Work with engineering teams to ensure data pipelines, analytics tools, ETL processes, and other systems are properly integrated with the Lyft Data Platform.
  • Troubleshoot and resolve data platform issues in a timely manner.
  • Participate in on-call rotations.
  • Develop and maintain monitoring and alerting systems to ensure platform availability and reliability.
  • Participate in code reviews, design discussions, and other collaborative team activities to maintain high-quality standards.
  • Continuously evaluate new technologies and tools to enhance the data platform.
  • Contribute to platform documentation, knowledge sharing, and best practice development.
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