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Design, develop, and maintain cloud-based data pipelines supporting enterprise reporting and analytics.
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Build and optimize ETL/ELT processes using AWS-native data services and modern data engineering practices.
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Integrate enterprise source systems, including Salesforce, NetSuite, and other business applications, into centralized data platforms.
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Develop and maintain scalable data models supporting reporting and business intelligence initiatives.
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Monitor pipeline performance, troubleshoot data issues, and implement improvements to ensure data accuracy and reliability.
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Partner with business stakeholders to understand reporting requirements and translate them into technical solutions.
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Support data governance initiatives by maintaining data quality, metadata, and documentation.
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Contribute to automation, continuous integration, and infrastructure-as-code practices to improve development efficiency.
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Participate in code reviews and promote engineering best practices across the team.
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Bachelor's degree in Computer Science, Information Systems, Engineering, or related field, or equivalent experience.
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2-4 years of experience in data engineering or cloud data platform development.
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Experience developing data pipelines using AWS services such as S3, Glue, Redshift, AppFlow, and Step Functions.
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Experience working with SQL, Python, and PySpark.
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Understanding of dimensional modeling, data warehousing concepts, and modern data lake technologies.
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Experience integrating enterprise applications such as Salesforce, NetSuite, or similar SaaS platforms.
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Familiarity with Git, CI/CD pipelines, and Infrastructure as Code (Terraform preferred).
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Practical experience with applying AI/ML techniques to data engineering problems, such as automated data quality checks, anomaly detection, or intelligent pipeline monitoring
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Strong analytical and problem-solving skills.
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Excellent collaboration and communication skills.
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Ensures Accountability
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Manages Complexity
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Communicates Effectively
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Balances Stakeholders
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Collaborates Effectively