全职 Enterprise Data and Analytics 工作, 薪水 up to SGD 5,000, ARISTON SERVICES PTE. LTD. Central Region (Singapore) 公司招聘中 - Ricebowl

SCOPE OF THE ROLE

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

TECHNICAL SKILLSETS

Certifications

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

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