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Senior Data Engineer

Roles & Responsibilities

  • 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

Requirements:

  • 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

Job description

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


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