Ensuring quality service output, submitted in a timely manner and communicated effectively
Continuous Improvement in Productivity to the standards prescribed from time to time
Manage data & information support for all the key initiatives across Retail Bank supporting countries & group teams.
Work closely with technology on solutions to resolve identified production issues which are impacting existing infrastructure /solution covering data quality, data assurance, refresh timeliness, data security governance
To work closely with Business & Technology to review the integration of new functionality / technology implementation projects initiated
Extend analytics support spanning across entire gamut of business (Customer Acquisition, Portfolio Management, New Product Development)
Manage analysis & reporting across all Retail products/segments
Interact with metrics owners across various functions and get end to end understanding of the metrics and applicability of the same across various MI dashboards
Provide development opportunities through regular engagement, ensuring that all staff is aware of the opportunities available in order to help them succeed within their role
Create and maintain optimal data pipeline architecture, assemble large, complex data sets that meet functional / non-functional business requirements.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Skills
8+ years of relevant technology experience
2+ years of experience working on retail banking domain / products: Credit Card, CASA (current and savings account), Loans Portfolio.
Strong analytic skills related to working with unstructured datasets.
Hands on Hadoop and Bigdata tech. e.g. HIVE, HBase, Kafka, Spark
Strong experience on SQL
Good Experience on Data Base & Data Ware house building and management
Exposure to data pipelines, architectures and data sets
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Build processes supporting data transformation, data structures, metadata, dependency and workload management.
Working knowledge of message queuing, stream processing and highly scalable big data data stores.