Senior Data Engineer (Batch and Streaming)

Role Overview

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.

   

Required Qualifications

Experience

• 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).

   

Streaming & Event-Driven Systems

• Experience with event streaming platforms such as Apache Kafka or Amazon Kinesis.

   

Data Architecture & Modeling

• Experience implementing lakehouse formats such as Delta Lake.

• Strong understanding of partitioning strategies and schema evolution.

   

Performance & Reliability

• 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.

   

DevOps & Platform Engineering

• Experience with Docker and CI/CD pipelines.
• Experience with Infrastructure as Code (Terraform, AWS CDK, or similar).
• Familiarity with monitoring and observability practices.