Senior Data Engineer

Here is a concise summary of the key points from the Senior Data Engineer job posting at Commit: 1. Key Technical Requirements and Skills: - 5+ years of data engineering experience - Strong hands-on expertise with Apache Spark, including Structured Streaming - Experience building both batch and streaming data pipelines in production - Proficiency in designing AWS-based data lake architectures using S3, EMR, Glue, Athena - Experience with event streaming platforms like Apache Kafka or Amazon Kinesis - Knowledge of lakehouse formats like Delta Lake - Proven track record in Spark performance tuning and cost optimization in AWS 2. Team/Project Information: - Building a greenfield analytics platform supporting batch and real-time data processing - Role combines hands-on development, architectural decision-making, and platform ownership 3. Unique/Notable aspects:

Description

We are building a greenfield analytics platform supporting both batch and real-time data processing. We are looking for a Senior Data Engineer who can design, implement, and evolve scalable data systems in AWS.

This role combines hands-on development, architectural decision-making, and platform ownership.

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.

Requirements

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 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

Originally posted on Himalayas