Summary
3 months contract then Hire(Basing requirement)
The
Sr. Gen AI Data Engineer will be responsible for designing, building, and maintaining scalable data infrastructure and pipelines that power AI/ML solutions across the enterprise. This role involves architecting robust data ecosystems, enabling high-quality data availability, and ensuring seamless integration with AI/ML workflows—particularly within
Retail Banking and Consumer Finance domains.
The ideal candidate will have strong expertise in
AWS cloud services (including S3, Redshift, EMR, Glue, Kinesis, and Lake Formation), advanced proficiency in
data modeling, data engineering, and pipeline orchestration, and hands-on experience delivering secure, compliant, and scalable data solutions for regulated financial environments.
Required Experience & Skills
- 8–12+ years of experience in data engineering, data architecture, or related roles.
- Deep proficiency in AWS Cloud, including architecture and optimization of data workloads using Glue, Redshift, EMR, S3, Kinesis, and Lake Formation.
- Strong understanding of Retail Banking and Consumer Finance data landscapes, regulatory requirements, and data governance frameworks.
- Expertise in Python, Spark, SQL, and pipeline orchestration tools (Apache Airflow, AWS Step Functions).
- Experience with real-time and batch data processing, event-driven architectures (Kafka/Kinesis), and data modeling techniques (Star Schema, Data Vault).
- Familiarity with DataOps practices, CI/CD for data pipelines, and integration with ML platforms such as SageMaker.
- Excellent communication and stakeholder management skills.
Roles & Responsibilities
Key Responsibilities:
- Design and build scalable, cloud-native data pipelines and ETL/ELT frameworks to support AI/ML and analytics initiatives across retail banking and consumer finance use cases such as credit decisioning, fraud detection, customer segmentation, and risk scoring.
- Architect and manage enterprise data lakes, data warehouses, and real-time streaming platforms leveraging AWS data services.
- Collaborate with AI Architects, Data Scientists, and business teams to ensure timely and high-quality data availability for model training, validation, and production consumption.
- Establish best practices for data governance, lineage, cataloging, security, and compliance aligned with financial industry standards.
- Lead POCs and evaluate emerging data engineering technologies to continuously modernize the data platform.
- Drive performance optimization of data pipelines, storage solutions, and query engines to support large-scale financial datasets.
