Description

You own end-to-end data pipelines, architecture, and production reliability for high-volume batch and streaming workloads.

This role is on-site.

Responsibilities

• Design, build, and operate critical batch and streaming pipelines using Glue, Redshift, Spark, Kinesis, and Lambda, handling schema evolution and late data.
• Define data architecture and Lakehouse patterns for specific business domains, selecting storage and query engines (S3, Redshift, Athena) based on performance needs.
• Tune Spark jobs, Glue configurations, and Redshift performance; investigate production issues and reduce latency and cloud costs.
• Implement domain-level data quality checks, monitoring, and alerting; own production incidents and drive permanent fixes.
• Lead by example through coding, PR reviews, and design guidance; mentor senior engineers and set engineering standards.

Required Skills

• 10+ years of experience in data engineering.
• Deep expertise with AWS services: Glue, Redshift, Spark, Kinesis, and Lambda.
• Strong experience with S3, Athena, and data modeling strategies.
• Proven ability to optimize complex ETL/ELT pipelines for high-volume workloads.
• Experience with schema evolution, CDC, and handling edge cases in distributed systems.
• Ability to translate business requirements into scalable, reliable data solutions.
• Bachelor’s degree in a relevant field or equivalent experience.

Key Skills

Education

Any Gradute