Summary:
The Sr. 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.