SYSTEM ANALYST - DATA

You will be responsible for end-to-end delivery of enterprise data and analytics solutions leveraging traditional and modern Data architecture. Experience with Financial Crime Analytics, Finance & Credit Risk Analytics, Credit Scoring & Decision systems, Retail & Wholesale Datamarts will of advantage

The role spans requirements analysis, solution design, testing, implementation, and production support ensuring high-quality, scalable, and compliant data platforms that support advanced analytics and AI initiatives.

Key Responsibilities

• Lead end-to-end solution delivery for data and analytics across the full SDLC.

• Analyze business and regulatory requirements, translate them into scalable solution designs & provide estimations.

• Communicate complex technical and architectural concepts to business and senior stakeholders in a clear, simplified manner.

• Review and approve test strategies, functional test cases, and data validation approaches.

• Manage risks and issues related to scope, data quality, regulatory commitments, and delivery timelines.

• Participate in product and platform evaluations (RFPs, PoCs) for data, analytics, and AI tooling.

• Partner with production support team to conduct root cause analysis, resolution, and preventive controls.

• Lead innovation and modernization initiatives, including data discovery, cataloguing, governance, and AI enablement.

• Drive productivity, efficiency & quality improvements across delivery and operational processes.

• Ability to design data architectures supporting NLP and AI-driven analytics.

FUNCTIONAL SKILLSETS

Analytics Domains

• Financial Crime Analytics Transaction Monitoring, Customer Due Diligence, Sanctions & Payments Screening

• Finance & Credit Risk Analytics Financial reconciliation, Allocation, Performance management, Regulatory and Management reporting, Credit risk exposure, NPL, Counterparty risk, Basel & IFRS9 input variables

Enterprise Data, Analytics & Unstructured Data Enablement

Proven experience delivering large-scale analytics platforms within financial services spanning structured, semi-structured, and unstructured data

• Strong capability in requirements analysis and functional design for analytics use cases involving Transactional data, Investigator narratives, Case notes and alerts, Policy & Customer communications documents

• Experience defining data quality, governance, lineage, and reconciliation controls for both structured and NLP-derived datasets.

Unstructured Data & NLP-Enabled Analytics

• Ability to define data architectures and data flows that ingest, curate, and govern unstructured and semi-structured data within enterprise data platforms.

• Experience translating business requirements into NLP-enabled analytical use cases, such as Text classification and categorization, Entity & relationship extraction, Risk indicator identification, Summarization of alerts, cases, or documents

Knowledge Graph & Relationship‑Based Analytics

• Ability to design and govern an enterprise knowledge layer defining relationship taxonomies, entity resolution rules, and linkage logic

• Ability to translate use cases into relationship‑driven analytical designs, such as Network‑based risk identification, Hidden association and indirect exposure analysis, Related‑party and concentric risk detection

Data Platforms & Architecture

• Open table formats: Apache Iceberg, Delta Lake, Apache Hudi

• Distributed processing & query engines: Spark, Trino/Presto, Hive

• Cost optimization strategies: tiered storage, lifecycle management, workload governance

Programming & Analytics

• SQL, BTEQ, GCFR

• Python (Pandas, NumPy)

• BI & visualization tools: Power BI, QlikSense

Data Integration & Quality

• Informatica suite: PowerCenter, BDM, IDQ, Enterprise Data Catalogue

• Data ingestion patterns: batch, CDC, streaming

• Data validation, quality controls, and reconciliation frameworks within environments

Governance, Risk & Compliance

• Data modelling, critical data elements, regulatory reporting

• Fine-grained data access controls (row-level, column-level, masking)

• Metadata management, lineage, and impact analysis

• Compliance with BCBS 239, MAS, AML/CFT, and internal data standards

Big Data Platforms

• Cloudera Hadoop distribution: Hive, Impala, Spark, Iceberg, Trino

At least two relevant technical certifications across data platforms, cloud, or analytics technologies.