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Senior Data Engineer

Posted 2 days agoViewed

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💎 Seniority level: Senior, 10+ YOE

🔍 Industry: Healthcare

🏢 Company: Prompt👥 101-250💰 $2,238,718 Seed over 4 years agoEducationiOSService IndustryUniversitiesSoftware

🗣️ Languages: English

⏳ Experience: 10+ YOE

Requirements:
  • 10+ YOE working with cloud based databases, data lakes, data warehouses (S3, RDS, AWS Athena, AWS Redshift etc.)
  • Proficiency in data engineering tech stack; for example; Athena / Redshift / Glue / MySQL / Python / Spark / Kafka / SQL / AWS / Airflow/ DBT / containers and orchestration (Docker, Kubernetes) and others
  • Experience and understanding of distributed systems, data architecture design, and big data technologies
  • Experience with AWS technologies ( e.g., AWS Lambda, Redshift, RDS, S3, DMS, Glue, Kinesis, SNS etc.)
  • Knowledge of data quality management, data governance, and data security best practices
  • Good knowledge of DevOps engineering using Continuous Integration/Delivery tools like Kubernetes, Jenkins, Terraform, etc., thinking about automation, alerting, monitoring, security, and other declarative infrastructure
  • Experience with managing multiple data pipelines for internal and external data products/ pipelines
Responsibilities:
  • Creating robust and scalable architectures to manage the flow and storage of data across the organization.
  • Developing and managing Extract, Transform, Load processes to ensure data is accurately integrated from various sources into data warehouses or lakes.
  • Constructing automated pipelines for data processing and transformation, ensuring smooth data flow and timely availability for analysis.
  • Administering databases and ensuring their performance, integrity, and security.
  • Implementing data validation, cleansing, and governance practices to maintain high-quality and reliable data.
  • Working closely with AI engineers, BI engineers, Analysts, and business stakeholders to understand data requirements and provide support in their analytical tasks. Participate in code reviews and architecture discussions to exchange actionable feedback with peers. Contribute to engineering best practices and mentor junior team members. Help break down complex projects and requirements into sprints.
  • Monitoring and optimizing data systems for improved performance and efficiency.
  • Evaluating and integrating new tools and technologies to enhance data management capabilities.
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