Integration & Data Movement Engineer

Integration & Data Movement Engineer

(Enterprise Integrations & Data Flow Enablement)

Role Overview
We are seeking a highly skilled Integration & Data Movement Engineer to design, implement, and operate reliable data flows between enterprise platforms. This role is responsible for ensuring that data moves accurately, securely, and efficiently across systems such as CRM, ERP, finance, HR, analytics, and external partner platforms.

In addition to strong integration expertise, this role requires deep experience with modern data engineering practices, including Snowflake, Medallion Architecture (Bronze, Silver, Gold layers), data ingestion, transformation, and orchestration. The engineer will work with structured and semi-structured data from APIs, databases, files, and third-party platforms, ensuring high-quality and scalable data movement solutions.

Key Responsibilities
• Design and implement integrations and ELT/ETL pipelines across APIs, databases, file-based sources, SaaS platforms, and enterprise applications.
• Build and maintain Snowflake-based data ingestion and transformation processes.
• Apply Medallion Architecture principles to organize data across raw, curated, and business-ready layers.• Develop data transformation logic using both SQL and Python, selecting the best tool based on business and technical requirements.
• Create scalable and reusable ingestion frameworks supporting batch and incremental loads.
• Implement data quality checks, validation rules, monitoring, and error handling.
• Partner with analytics and reporting teams to ensure data is trusted and optimized for downstream consumption.
• Document integration designs, lineage, dependencies, and operational procedures.

Required Qualifications
• 5–10+ years of experience designing and implementing system integrations and data pipelines.
• Strong SQL expertise including Stored Procedures.• Strong Python development skills for data ingestion, transformation, automation, and operational support.

• Hands-on experience with Snowflake.

• Experience ingesting and transforming data from APIs, relational databases, flat files, and external systems.
  • Experience implementing ELT/ETL solutions and data quality controls.
  • Strong understanding of REST/SOAP APIs, authentication mechanisms, and integration patterns.
  
  Preferred Qualifications
  • Experience with DBT, including models, testing, lineage, documentation, and data quality frameworks.
  • Experience with cloud-native integration patterns and services (Azure, AWS, or GCP).
  • Familiarity with Kafka, Service Bus, or other event streaming technologies.
  
  What Success Looks Like
  • Reliable, scalable, and well-governed data pipelines supporting enterprise reporting and analytics.
  • Consistent application of Medallion Architecture and data engineering best practices.
  • Trusted, documented, and testable transformation logic with strong lineage visibility.
  • Efficient onboarding of new data sources with minimal manual intervention.

Originally posted on Himalayas