Job Description :

Key highlights of the role are listed below (purely indicative and not limiting) :

- Design, build, and completely own scalable data models and resilient data infrastructure optimized for modern cloud data environments (evaluating GCP and AWS).

- Develop and maintain reliable ELT pipelines to transform, curate, and enrich large-scale credit card datasets starting immediately from the post-landing stage.

- Lead the end-to-end logical and physical data modeling strategy for the analytics datamart across Bronze, Silver, and Gold operational frameworks.

- Provide technical governance over cloud architecture selection (GCP vs. AWS evaluation), guaranteeing compliance with data localization mandate.

- Establish security boundaries including VPC controls, row/column-level access management, and automated PII masking frameworks.

- Lead Insights to translate raw data into active strategies, tracing transition matrices across different verticals.

Applicants should possess the following attributes :

- Solid, expert-level understanding of database internals, query execution plans, performance optimization, and large-scale data modeling concepts (Star/Snowflake, conformed dimensions).

- Exceptional capacity to synthesize complex technical data structures into executive-level, non-technical strategic business briefs.

- Expertise in credit card or retail lending data architectures (delinquency aging buckets, provisioning logic, risk personas).

- Programming & Modeling: Strong hands-on mastery of Python & SQL.

- Transformation & Orchestration: Production-grade expertise using dbt (Data Build Tool) and Apache Airflow to manage complex, multi-layered dependency DAGs.

- Cloud Ecosystems (GCP or AWS compatibility) :

1. GCP Ecosystem: BigQuery, Cloud Storage, Dataflow, Cloud Composer, Cloud DLP.

2. AWS Ecosystem: Amazon Redshift, S3, AWS Glue, Managed Airflow (MWAA), AWS Macie.