Design, develop, and maintain cloud-based data pipelines supporting enterprise reporting and analytics.

Build and optimize ETL/ELT processes using AWS-native data services and modern data engineering practices.

Integrate enterprise source systems, including Salesforce, NetSuite, and other business applications, into centralized data platforms.

Develop and maintain scalable data models supporting reporting and business intelligence initiatives.

Monitor pipeline performance, troubleshoot data issues, and implement improvements to ensure data accuracy and reliability.

Partner with business stakeholders to understand reporting requirements and translate them into technical solutions.

Support data governance initiatives by maintaining data quality, metadata, and documentation.

Contribute to automation, continuous integration, and infrastructure-as-code practices to improve development efficiency.

Participate in code reviews and promote engineering best practices across the team.

Bachelor's degree in Computer Science, Information Systems, Engineering, or related field, or equivalent experience.

2-4 years of experience in data engineering or cloud data platform development.

Experience developing data pipelines using AWS services such as S3, Glue, Redshift, AppFlow, and Step Functions.

Experience working with SQL, Python, and PySpark.

Understanding of dimensional modeling, data warehousing concepts, and modern data lake technologies.

Experience integrating enterprise applications such as Salesforce, NetSuite, or similar SaaS platforms.

Familiarity with Git, CI/CD pipelines, and Infrastructure as Code (Terraform preferred).

Practical experience with applying AI/ML techniques to data engineering problems, such as automated data quality checks, anomaly detection, or intelligent pipeline monitoring

Strong analytical and problem-solving skills.

Excellent collaboration and communication skills.