Senior Data Engineer

We are looking for a Senior Data Engineer who can design, implement, and evolve scalable data systems in AWS.

Core Responsibilities

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.

Required Qualifications

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 deg 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.