Description

You will design and maintain scalable data pipelines and enterprise data platforms across AWS, supporting Customer 360 solutions and privacy compliance.

This role is on-site.

Responsibilities

• Design and develop scalable data pipelines using Spark (Scala/Python), AWS Glue, AWS Lambda, and Shell scripting.
• Build and optimize ETL/ELT workflows across Amazon S3, Hive, Apache Iceberg, and Amazon Redshift.
• Develop Redshift SQL/PLSQL code, stored procedures, and enterprise data processing frameworks.
• Design ingestion frameworks for structured and semi-structured data formats including JSON, CSV, XML, ORC, Parquet, and Avro.
• Troubleshoot complex data quality and platform issues using Redshift SQL, MySQL, Python/PySpark, and perform root cause analysis.

Required Skills

• 12+ years of experience in Data Warehouse, Big Data, or Enterprise Data Engineering.
• 5+ years in a Lead Data Engineer, Technical Lead, or SME role.
• Strong experience with AWS services: S3, EC2, Redshift, Glue, Lambda, Kafka, Airflow, Hive, and Apache Iceberg.
• Proficiency in Spark (Scala/Python) for enterprise data pipeline development.
• Experience optimizing Redshift SQL/PLSQL, MySQL, stored procedures, and database performance.
• Strong understanding of dimensional modeling, distributed data processing, and cloud-native data platforms.
• Experience with Customer 360 or customer data platforms.
• Knowledge of data governance, privacy compliance, and enterprise data security principles.

Preferred Skills

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related field, or equivalent professional experience.
• Experience with MicroStrategy Privacy Compliance module.
• Ability to conduct code reviews and provide technical guidance to engineering teams.

Key Skills

Education

Bachelor's degree