Вакансия Senior Data Engineer

Skills

• AWS
• AWS Glue
• Amazon S3
• Athena
• EMR
• EC2
• MWAA
• Python
• PySpark
• SQL
• Scala
• Apache Spark
• Trino
• Presto
• ETL
• Great Expectations
• Soda

Описание вакансии

Essential functions

• Design and optimize data ingestion systems to ensure a consistent flow of high-quality data into the platform, creating a solid foundation for developing data products and supporting comprehensive KPI tracking and analysis.
• Demonstrate expertise in Infrastructure as Code (IaC), utilizing tools like Terraform or CloudFormation to automate infrastructure deployment and management.
• Translate business requirements into robust technical architecture, including designing physical schema and logical data models.
• Engage with stakeholders to understand their needs, providing technical guidance for current and future data platform projects.
• Analyze user interaction with the data platform, focusing on patterns of use to identify areas for improvement and optimize user engagement over time.
• Implement and maintain data governance frameworks, emphasizing automated processes for data quality checks, compliance adherence, and secure data handling, while collaborating with engineering teams to integrate governance protocols into data pipelines and platform architecture.
• Participate actively in all Data Platform Engineering team meetings and knowledge-sharing sessions, contributing to team learning and process improvement.

Qualifications

• AWS Services: Expertise in AWS services, especially Glue, S3, Athena, EMR, EC2, and MWAA.
• Programming Languages: Proficiency in Python, PySpark, SQL, and/or Scala.
• Big Data Technologies: Hands-on experience with Spark, and Trino, Presto
• Data Platforms: Experience in building data platforms, not just using them. (MUST HAVE)

Would be a plus

• Data Engineering: 3+ years of work experience as a Data Engineer in a cloud-based environment, with a focus on AWS.
• ETL and Data Modeling: Advanced understanding of ETL processes, data modeling techniques, and best practices.
• Data Governance and Quality: Experience in applying data governance policies, implementing data contracts, and using data quality frameworks like Great Expectations and Soda.
• Data Architecture: Strong understanding of data architecture to enable ML and data analytics.
• Data Mesh: Familiarity with data mesh architecture and principles, with an appreciation for decentralized data management and shared data ownership.