Description

You will reverse engineer legacy ETL pipelines and modernize data infrastructure across distributed systems.

This role is on-site.

Responsibilities

• Reverse engineer SSIS, Spark, and stored procedures to document data flows, source-to-target mappings, and business logic.
• Refactor ETL pipelines for 1:1 functional parity during migrations, ensuring schema evolution and zero-downtime transitions.
• Build modular, reusable pipelines using Spark, PySpark, and Scala, orchestrating workflows via Airflow DAGs.
• Optimize data storage in Hive, MongoDB, and SQL Server by tuning partitioning, indexing, sharding, and file formats (Parquet, ORC, Avro).
• Implement comprehensive logging, error handling, alerting, and metadata capture for all data workflows.

Required Skills

• 5+ years of experience in ETL migration and remediation projects.
• Strong proficiency in Python, PySpark, Scala, and Hadoop ecosystem tools.
• Expertise in reverse engineering legacy ETL (SSIS, Spark, scripts) and data modeling (dimensional, normalized, denormalized).
• Hands-on experience with MongoDB (indexing, sharding, collections) and SQL Server.
• Proficiency in orchestration frameworks, specifically Apache Airflow.
• Experience with CI/CD tools including Jenkins, Docker, and Infrastructure as Code (IaC).
• Bachelor's degree in Computer Science or related field.

Preferred Skills

• Experience with AI/ML-assisted ETL remediation or code conversion.
• Familiarity with CSPM/CNAPP domains (Wiz, Palo Alto Prisma) and regulated, compliance-heavy environments.

Key Skills

Education

Bachelor's degree