Flink Data Engineer

Devire Outsourcing IT is a collaboration model dedicated to IT specialists based on B2B contracts, delivering projects for clients running innovative and modern projects. The client is a consulting and technology company specializing in digital transformation, software engineering, cloud, and data.

• Minimum 5 years of experience in data engineering, analytics engineering, or related fields
• Practical experience with Google BigQuery and Looker (LookML) in production environments
• Very good knowledge of SQL
• Experience in data modeling
• Knowledge of dbt or similar data transformation tools
• Good understanding of data governance, lineage, and data documentation
• Communication and collaboration skills
• Practical experience using AI assistants (e.g., Claude Code, GitHub Copilot, Cursor) to enhance productivity, quality, or decision-making in software development
• Practical experience with Apache Flink (including DataStream API)
• Experience in maintaining and updating Flink environments (experience with Flink 2.0 is a plus)
• Deep understanding of streaming pipeline architectures
• Knowledge of performance optimization, state management, and fault tolerance mechanisms
• Experience migrating large datasets from BigQuery to Data Cloud Storage
• Very good knowledge of data format conversion (especially Avro to Parquet)
• Ability to design, scale, and automate migration processes
• Attention to data integrity and minimizing downtime
• Good knowledge of Google Cloud Platform (GCP) and its data services
• Understanding of distributed systems
• Knowledge of schema evolution and data storage optimization
• Ability to break down complex problems into concrete, actionable steps
• Proactivity and sense of responsibility for solutions
• Ability to identify risks

• Development and maintenance of real-time data processing pipelines using Apache Flink
• Migration of existing Flink jobs (DataStream API) and adaptation to new platform standards
• Planning and execution of Apache Flink platform upgrade to version 2.0
• Designing and optimizing efficient, scalable, and fault-tolerant streaming architectures
• Migration of large datasets from BigQuery (BQ) to Data Cloud Storage (DCS)
• Automation and scaling of data migration processes to handle increasing volumes
• Data conversion (Avro to Parquet) considering performance, schema evolution, and storage optimization
• Use of AI tools to improve migration, validation, and data transformation processes
• Ensuring high data quality and integrity and minimizing system downtime
• Monitoring and optimizing pipeline and streaming platform performance
• Collaboration with technical and business (cross-functional) teams
• Communicating technical issues clearly to non-technical stakeholders