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Metro location and known employer create moderate competition, tempered by senior, specialized GCP ML-data requirements.
Strong GCP, BigQuery, and ML-platform expertise required, making background fit highly domain-specific.
Explicit 10+ years and deep GCP/ML pipeline requirements make shortlisting highly strict.
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Lead design and implementation of scalable, ML-ready data architectures and pipelines on Google Cloud Platform leveraging services like BigQuery, Dataproc, and Data Fusion.
Partner with Data Scientists and ML Engineers to deliver trusted, reproducible datasets and support ML workflows including feature engineering, model training, and inference.
Drive data platform architecture strategies, modernization of legacy ETL to ELT pipelines, and establish standards for data quality, governance, and operational excellence.
10+ years experience in data engineering, data platforms, or data warehousing with strong focus on cloud-native architectures.
Proven expertise in designing and implementing large-scale data pipelines and platforms on Google Cloud Platform (GCP).
Hands-on proficiency with Python, SQL, Spark, and GCP services such as BigQuery, GCS, Dataproc, Composer, Dataform, and Data Fusion.
Work Experience Required: 10+ years in relevant data engineering roles. Notice period: Not explicitly mentioned in the JD.
Senior technical leader with deep practical experience enabling production ML/AI solutions in cloud environments, especially GCP.
Experienced in collaborating cross-functionally with Data Scientists, ML Engineers, and Product Owners to translate model/business requirements into scalable data solutions.
Strong focus on building robust, cost-efficient, and maintainable data products supporting analytics, AI, GenAI, and operational ML workloads.