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Senior Data Engineer

Posted about 13 hours agoViewed

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💎 Seniority level: Senior, 5+ years

📍 Location: Madrid, Barcelona

🏢 Company: Clarity AI

🗣️ Languages: English

⏳ Experience: 5+ years

🪄 Skills: DockerPythonSQLApache AirflowCloud ComputingKubernetesAlgorithmsData engineeringData StructuresPostgresREST APICommunication SkillsAnalytical SkillsCI/CDProblem SolvingAgile methodologiesMicroservicesData visualizationData modelingData analyticsData management

Requirements:
  • 5+ years in data architecture, data engineering, or a related role, with hands-on experience in data modeling, schema design, and cloud-based data systems
  • Strong Python and SQL skills and proficiency with distributed data stores
  • Proficiency in schema design, data modeling, and building data products
  • Proficient in at least one major programming language, preferably Python, with a software engineering mindset for writing clean, maintainable code
  • Deep understanding of architectural principles in microservices, distributed systems, and data pipeline design
  • Familiarity with containerized environments, public/private API integrations, and security best practices
  • Strong communication and interpersonal skills, with experience working cross-functionally
  • Proven ability to guide teams and drive complex projects to successful completion
  • Self-starter, able to take ownership and initiative, with high energy and stamina
  • Decisive and action-oriented, able to make rapid decisions even when they are short of information
  • Highly motivated, independent and deeply passionate about sustainability and impact
  • Excellent oral and written English communication skills (minimum C1 level-proficient user)
Responsibilities:
  • Transforming raw data into intuitive, high-quality data models
  • Implementing and monitor data quality checks
  • Collaborating across functions
  • Working closely with engineering and product teams
  • Acting as a bridge between technical and non-technical stakeholders
  • Leading initiatives to improve data practices
  • Guiding the team in experimenting with new tools and technologies
  • Continuously evolving the data architecture
  • Applying a pragmatic approach to performance metrics and scaling decisions
  • Implementing performance metrics to monitor system health
  • Maintaining comprehensive documentation of data systems, processes, and best practices
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