Senior Data Engineer

• Design and implement batch and streaming data pipelines using Apache Spark.
• Build and evolve a scalable AWS-based data lake architecture.
• Develop and maintain real-time data processing systems (event-driven pipelines).
• Own performance tuning and cost optimization of Spark workloads.
• Define best practices for data modeling, partitioning, and schema evolution.
• Implement monitoring, observability, and data quality controls.
• Contribute to infrastructure automation and CI/CD for data workflows.
• Participate in architectural decisions and mentor other engineers.

• 5+ years of experience in Data Engineering.
• Strong hands-on experience with Apache Spark (including Structured Streaming).
• Experience building both batch and streaming pipelines in production environments.
• Proven experience designing AWS-based data lake architectures: S3, EMR, Glue, Athena.
• Experience with event streaming platforms such as Apache Kafka or Amazon Kinesis.
• Experience implementing lakehouse formats such as Delta Lake.
• Strong understanding of partitioning strategies and schema evolution.
• Experience using SparkUI and AWS CloudWatch for profiling and optimization.
• Strong understanding of Spark performance tuning (shuffle, skew, memory, partitioning).
• Proven track record of cost optimization in AWS environments.
• Experience with Docker and CI/CD pipelines.
• Experience with Infrastructure as Code: Terraform, AWS CDK.
• Familiarity with monitoring and observability practices.
• Experience in the Financial domain.
• Experience running Spark workloads on Kubernetes.
• Experience implementing data quality frameworks or metadata/lineage systems.
• English - B2, Ukrainian- Native

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