Description

You will design, build, and maintain data processing pipelines and infrastructure to support data science initiatives.

Responsibilities

• Design and develop data processing pipelines using Python, R, or Scala.
• Define APIs and microservices for consumption by other applications and teams.
• Build, test, and maintain tools and infrastructure for data ingestion and ETL processes.
• Monitor live data pipelines and implement changes in production transactional systems to support ETL.
• Implement solutions to resolve data quality issues and ensure data security and disaster recovery across cloud regions.

Required Skills

• 5+ years of software and data engineering experience with large-scale data processing systems.
• 3-5 years of technical design experience using SQL, Python, and R.
• 3-5 years of experience managing customer data flows across CDP, Salesforce Marketing Cloud, Salesforce Service Cloud, and MuleSoft APIs.
• Proficiency with big data technologies including Spark, Hive, Parquet, Distributed File Systems, and column stores.
• Hands-on experience with containerization and orchestration using Docker and Kubernetes.
• Experience with end-to-end data pipeline automation, including quality validation and reporting.
• Strong understanding of DevOps, Data Engineering, and ML fundamentals.
• Working knowledge of Agile methodologies.
• Bachelor’s Degree in Computer Science, Information Technology, or a closely related field.

Key Skills

Education

Any Graduate