Lead the design and implementation of enterprise‑level data architecture for large-scale banking and financial systems. Develop comprehensive data models (conceptual, logical, physical) to support transactional, operational, and analytical workloads. Architect and optimize data transformation, data integration, and ETL/ELT pipelines using Informatica or Talend. Design scalable data storage, archival, retention, and retrieval solutions across PostgreSQL, Oracle, and Snowflake. Perform data performance engineering, including SQL optimization, indexing, and partitioning for high-volume systems. Create, refine, and validate complex SQL queries, scripts, and stored procedures supporting business analytics and reporting. Build and support enterprise dashboards by enabling Power BI semantic models and performance tuning. Develop and automate Unix Shell Scripts for data ingestion, scheduling, and workflow orchestration. Collaborate with cross-functional teams to support business, regulatory, risk, fraud, and compliance reporting needs. Troubleshoot and resolve data issues related to ETL pipelines, storage performance, and analytic workloads. Participate in Agile ceremonies including sprint planning, backlog refinement, and architectural review sessions. Ensure enterprise systems meet data security, audit, privacy, and retention requirements aligned with banking regulations. 10+ years of enterprise-level experience in data architecture, data modeling, data transformation, data storage, data archival, and performance engineering. 5+ years of advanced hands-on experience with Informatica or Talend for ETL/ELT development 7+ years experience with relational and cloud data platforms such as PostgreSQL, Oracle, and Snowflake 8+ years experience writing and tuning Advanced SQL for large-scale datasets (minimum 8+ years). 5+ years experience creating and optimizing Power BI dashboards, semantic models, and DAX expressions. Strong proficiency in Unix Shell Scripting for automation and data workflows (minimum 5+ years). Deep understanding of enterprise data governance, data quality, metadata management, and lineage tracking. Excellent communication, documentation, and analytical skills required to support large banking/financial organizations. Banking or Financial Services domain experience. Exposure to Python, Databricks, and Apache Spark. Experience with cloud-based data platforms and distributed compute environments. Project lead or data architecture leadership experience.