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Tier-1 brand, mid-level generalist data role, metro locations, and popular Databricks/Spark skills increase competition.
Core data engineering skills transfer across industries, though financial data sensitivity increases domain bias.
Explicit 2+ years and specific Databricks/PySpark/AWS/Kubernetes requirements enforce strict filters.
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Design, develop, and troubleshoot data engineering solutions focusing on secure, stable, and scalable data collection, storage, and access.
Maintain and update data models and data quality checks with minimal supervision, incorporating AI-assisted approaches to enhance data analysis and validation.
Make custom configuration changes in tools to deliver data products aligned with business or customer requests.
2+ years of applied experience in data engineering with formal training or certification in data engineering concepts.
Experience with ETL data pipelines, batch and real-time data processing using Spark or Flink, and working knowledge of Databricks, Python/Java, and PySpark.
Working knowledge of AWS Glue, EMR, and deploying services on AWS EKS or Kubernetes using frameworks like Spring Boot or Flask.
Work Experience Required: Minimum 2 years applied experience in data engineering.
Experienced with both relational and NoSQL databases and data warehousing, capable of handling data lifecycle and data frameworks independently.
Proficient in utilizing enterprise-authorized AI tools for data engineering workflows with strong validation and data sensitivity awareness.
Familiarity with cloud-native environments and containerized data solutions using AWS, Docker, Kubernetes, and big data technologies such as Hadoop, Spark, and Kafka.