AI GCP Data Engineer

We are looking for a hands-on Data Engineer with 3 years of experience building production-grade data pipelines cloud data platforms and automated data workflows. You are comfortable working across structured semi-structured and unstructured data; you understand the importance of data quality lineage security and cost optimization; and you are excited to build the data foundation required for modern AI ML and GenAI use cases.

• Understand business analytics and AI use cases and translate them into scalable data engineering solutions.
• Design build and maintain reliable batch andstreamingdata pipelines for ingestion transformation validation and publishing.
• Develop curated reusable and well-documented data products that support BI dashboards analytics applications ML models and GenAI-enabled solutions.
• Implement strong data quality checks observability lineage metadata management and monitoring practices to improve trust in enterprise data assets.
• Write clean modular and well-tested code using Python SQL and modern data engineering frameworks.
• Use cloud-native technologies such asBigQuery DataflowDataproc Cloud Composer/AirflowDataform DBT Spark or equivalent tools to deliver resilient data solutions.
• Enable AI/ML and GenAI teams by preparing high-quality feature datasets vector-ready datasets document corpora and governed data access patterns.
• Partner with data scientists ML engineers product owners and business stakeholders to support experimentation model deployment and production analytics.
• ApplyDataOpspractices including CI/CD version control automated testing reusable templates release management and production support standards.
• Optimize pipeline performance storage usage compute cost and reliability across cloud-based data platforms.
• Support data governance privacy access control and compliance expectations for enterprise and AI-ready data assets.
• Stay current with advances in cloud data engineering AI data infrastructure orchestration data quality and GenAI-enabling technologies.

Minimum Qualifications:

• Bachelors orMasters degree in Computer Science Data Engineering Information Systems Engineering Statistics Mathematics or related technical field.
• 3 years of hands-on experience in data engineering ETL/ELT development data warehousing or cloud-based data platform delivery.
• Strong proficiency in SQL and Python for data extraction transformation automation testing and production support.
• Experience designing and operating scalable pipelines on cloud platforms such as Google Cloud Platform AWS Azure or equivalent enterprise data ecosystems.
• Experience with modern data platforms and tools such asBigQuery Spark DataflowDataproc Airflow/Cloud ComposerDataform DBT or similar technologies.
• Good understanding of data modeling dimensional modeling partitioning clustering performance tuning and cost optimization.
• Working knowledge of data quality frameworks monitoring alerting metadata lineage and production support practices.
• Familiarity with Git CI/CD agile delivery code reviews documentation and reusable engineering standards.

• Strong communication skills with the ability to explain technical solutions clearly to engineering analytics and business stakeholders.

  Preferred Qualifications:

• 5 years of experience delivering enterprise data engineering solutions in cloud-native environments.
• Experience building data products for AI/ML GenAI semantic search retrieval-augmented generation feature engineering or model monitoring use cases.
• Experience working with unstructured data such as documents logs text images transcripts or embeddings and preparing them for downstream AI consumption.
• Hands-on experience withDataOpsMLOpsenablement pipeline observability automated testing and production incident resolution.
• Experience migrating legacy workflows from Hadoop Alteryx oron-premiseplatforms to modern cloud services.
• Experience with APIs microservices event-driven architectures streaming data or real-time analytics.
• Cloud certifications in Google Cloud Platform AWS Azure or relevant data engineering technologies.
• Experience mentoring junior engineers defining engineering standards or contributing reusable platform accelerators.