Role Overview:

Responsible for end-to-end delivery of enterprise data and analytics solutions across traditional and modern data architectures. The role covers requirements analysis, solution design, testing, implementation, and production support, ensuring scalable, compliant, and high-quality data platforms that support advanced analytics and AI initiatives.

Experience in Financial Crime Analytics, Finance & Credit Risk Analytics, Credit Scoring & Decision Systems, and Retail & Wholesale Datamarts is advantageous.

Key Responsibilities:

Total Yrs. of Experience 8-12 years System Analysis

Lead end-to-end solution delivery across the full SDLC
Analyze business and regulatory requirements and translate them into scalable solution designs with estimations
Communicate complex technical and architectural concepts to business and senior stakeholders
Review and approve test strategies, functional test cases, and data validation approaches
Manage risks and issues related to scope, data quality, regulatory commitments, and timelines
Participate in product/platform evaluations (RFPs, PoCs) for data, analytics, and AI tools
Collaborate with production support for root cause analysis, resolution, and preventive controls
Drive innovation and modernization initiatives including data governance, cataloguing, and AI enablement
Improve productivity, efficiency, and quality across delivery and operations
Design data architectures supporting NLP and AI-driven analytics

Functional Skillsets:

Analytics Domains:

Financial Crime Analytics (Transaction Monitoring, CDD, Sanctions & Payments Screening)
Finance & Credit Risk Analytics (Reconciliation, Reporting, Credit Risk, Basel, IFRS9)

Enterprise Data & Analytics:

Experience delivering large-scale analytics platforms across structured, semi-structured, and unstructured data
Strong requirements analysis and functional design for analytics use cases involving transactional and textual data
Experience in data quality, governance, lineage, and reconciliation

Unstructured Data & NLP Analytics:

Design data architectures for unstructured and semi-structured data ingestion and governance
Translate business needs into NLP use cases such as text classification, entity extraction, risk identification, and summarization

Knowledge Graph & Relationship Analytics:

Define relationship taxonomies, entity resolution rules, and linkage logic
Design analytics for network-based risk identification, hidden associations, and exposure analysis

Technical Skillsets:

Certifications:

Minimum two relevant certifications in data platforms, cloud, or analytics

Data Platforms & Architecture:

Apache Iceberg, Delta Lake, Apache Hudi
Spark, Trino/Presto, Hive
Cost optimization (tiered storage, lifecycle management, workload governance)

Programming & Analytics:

SQL, BTEQ, GCFR
Python (Pandas, NumPy)
Power BI, QlikSense

Data Integration & Quality:

Informatica (PowerCenter, BDM, IDQ, Enterprise Data Catalogue)
Batch, CDC, and streaming ingestion
Data validation, quality, and reconciliation frameworks

Governance, Risk & Compliance:

Data modelling, regulatory reporting
Data access controls (row/column level, masking)
Metadata management, lineage, impact analysis
Compliance: BCBS 239, MAS, AML/CFT

Big Data Platforms:

Cloudera Hadoop (Hive, Impala, Spark, Iceberg, Trino)

Enterprise Data and Analytics – System Analyst