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Job Description
Structured overview of role & requirementsAbout This Role
Design, build, and maintain scalable batch and streaming data pipelines supporting AI, ML, and GenAI use cases.
Develop reusable data products that enable BI dashboards, analytics, and model deployment, ensuring high data quality, lineage, and cost optimization.
Collaborate with AI/ML teams, implement DataOps practices including CI/CD, and optimize cloud-based data platform performance and compliance.
Minimum Requirements
3+ years of hands-on data engineering experience including ETL/ELT and cloud data platform delivery.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or related technical field.
Proficiency in Python and SQL, with experience on Google Cloud Platform or equivalent enterprise cloud platforms like AWS or Azure.
Experience with tools such as BigQuery, Spark, Dataflow, Dataproc, Airflow/Cloud Composer, Dataform, or DBT and knowledge of data quality, monitoring, and production support practices.
Ideal Candidate Profile
Experienced in delivering enterprise-grade cloud-native data engineering solutions, especially for AI/ML and GenAI-focused products.
Skilled in building data products using structured and unstructured data for feature engineering and downstream AI/ML consumption.
Proficient with DataOps, MLOps enablement, cloud cost/performance optimization, and contributing reusable engineering standards or mentoring.