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eCloudvalley Technology (Philippines), Inc

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Cloud Engineer (Data/AI)

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Summary

Design, build, and maintain scalable data pipelines powering analytics and AI initiatives on AWS (Glue, Lambda, Redshift) and Snowflake, using Airflow, Kafka, dbt, Python, SQL, and Terraform. A senior individual-contributor role that also includes mentoring engineers and shaping the data platform's technical strategy.

We are looking for Cloud Engineer (Data/AI) to design, build, and maintain scalable data pipelines that power our analytics and AI initiatives. You'll own the full lifecycle - from infrastructure and orchestration to transformation and delivery - working across AWS-native services (Glue, Lambda, Redshift) and Snowflake to build enterprise-grade data platforms. You'll contribute to our platform's technical strategy and mentor engineers on the team.

Key Responsibilities:

  • Design and implement data pipelines using Airflow and Kafka, ensuring reliability, idempotency, and failure handling

  • Build and optimize data transformations in Snowflake and dbt; design schema architecture and data models

  • Develop AWS Glue jobs and Lambda functions for ETL/ELT workflows; integrate with Redshift and Snowflake

  • Write production Python and SQL; manage infrastructure-as-code (Terraform) on AWS

  • Optimize for cost and performance; conduct code reviews and establish best practices

  • Mentor junior engineers and partner with data analysts, BI engineers, and ML teams to define data requirements and SLAs

Required Qualifications:

  • 4+ years building data pipelines in production (Python, SQL, at least one orchestration tool)

  • Strong AWS fundamentals (EC2, S3, IAM, compute services); hands-on with Glue and Lambda

  • Snowflake architecture, performance optimization, and SQL development

  • Experience with dbt or similar transformation frameworks

  • Hands-on with Terraform.

  • Distributed processing with PySpark or Spark SQL

Nice to Have:

  • Event streaming platforms (Kafka, Kinesis)

  • Amazon Redshift design and optimization; Quick dashboard development

  • Data quality frameworks, testing, and monitoring

  • CI/CD and GitHub Actions;

  • Coaching or mentoring experience; knowledge of data governance and security

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See also

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