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Tier-1 bank, mid-level popular Data Engineer role with broad GCP/AI skills increases applicant competition.
Core data engineering skills transfer across industries, but banking compliance and enterprise scale raise domain specificity.
Explicit 4+ years plus mandatory GCP, Python, Spark, and data governance requirements make shortlisting strict.
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Lead the development and modernization of scalable data platforms on Google Cloud Platform (GCP) focused on AI/GenAI-driven data solutions.
Design, build, and maintain optimized data pipelines and enterprise-scale ETL/ELT and streaming solutions using Python, SQL, Spark, and GCP services such as BigQuery, Dataproc, Composer, Kafka, and Pub/Sub.
Drive cloud migration, data transformation, data quality initiatives, and implement metadata, lineage, governance, and observability practices, collaborating with cross-functional teams to deliver scalable, reliable AI-enabled data products and services.
Minimum 4 years of Data Engineering experience or equivalent demonstrated through work experience, training, education, or military experience.
Strong hands-on expertise in Python, SQL, Spark, and data engineering.
Experience with GCP services including BigQuery, Dataproc, Cloud Composer, Pub/Sub, Kafka.
Proficiency in building large-scale ETL/ELT and streaming data pipelines with knowledge of data modeling, governance, and cloud architecture.
Experienced in leading moderately complex data engineering initiatives within enterprise environments involving AI and GenAI data solutions.
Skilled in integrating modern AI technologies such as large language models, vector search, semantic search, and AI-powered applications.
Operates with a focus on engineering excellence through automation, CI/CD, monitoring, reusable frameworks, and collaborative delivery with product, platform, security, and business teams.