- Singapore
Kerja Sepenuh Masa Principal Data Business Analyst, Gaji tinggi SGD 19,000 di KSYS Solutions - Maukerja
Kongsi
Simpan
Lokasi Kerja
Penerangan Kerja
Tanggungjawab
Principal Data Business Analyst – Corporate Banking & Data Warehousing
Position Overview
We are seeking an executive-level
Principal Data Business Analyst
to drive the modernization, architecture, and functional alignment of our Corporate Banking Enterprise Data Platform. In this high-impact role, you will bridge the gap between complex corporate banking business lines (Cash Management, Trade Finance, Lending, Treasury) and modern lakehouse data architectures.
With over 15 years of deep expertise in banking data warehousing (EDW), data modeling, and business intelligence, you will act as the technical and domain authority in migrating and optimizing end-to-end data pipelines onto
Databricks
.
Key Responsibilities
1. Corporate Banking Business & Functional Analysis
Partner with senior business stakeholders across Corporate & Investment Banking (CIB) to elicit, analyze, and formalize complex data requirements for regulatory reporting, risk management, credit risk, and commercial analytics.
Translate business logic, complex financial calculations, and domain workflows into clear functional requirement documents (FRDs), source-to-target mappings (STTM), and data dictionary definitions.
Define and govern KPIs, data lineage, and business definitions for core corporate banking products (e.g., Syndicated Loans, Letters of Credit, Wire Transfers, Nostro/Vostro accounting).
2. Databricks & Lakehouse Integration
Lead the functional design and data transformation logic for modernizing legacy banking data warehouses (Oracle, Teradata, DB2) onto modern
Databricks Lakehouse (Spark, Delta Lake, Unity Catalog)
.
Design and validate PySpark/SQL-based data transformations, aggregation logic, and feature engineering specifications within Databricks environments.
Leverage
Databricks Unity Catalog
to assist in defining automated data governance, fine-grained access control policies, and auditability mechanisms tailored to banking compliance standards.
3. Data Warehousing & Architecture Strategy
Oversee dimensional and relational data modeling initiatives (Kimball/Inmon methodologies, Third Normal Form, Star/Snowflake schemas) optimized for high-volume corporate transaction datasets.
Collaborate closely with Enterprise Data Architects and ETL/Data Engineers to design scalable, near-real-time data pipelines and batch processing schedules.
Drive end-to-end Data Quality Management (DQM), root-cause anomaly analysis, reconciliation frameworks, and User Acceptance Testing (UAT) strategies.
4. Leadership & Stakeholder Management
Act as the lead bridge between executive business leaders, regulatory compliance officers, and core technical engineering squads.
Mentor senior/junior business analysts and data engineers on domain concepts, data warehouse standards, and modern data stack paradigms.
Required Qualifications & Experience
Experience:
15+ years
of progressive experience as a Data Business Analyst, Data Architect, or Systems Analyst within global corporate banking or enterprise financial services environments.
Data Warehousing Expertise:
Deep background in enterprise data warehousing (EDW) lifecycle—from legacy relational architectures (Teradata, Oracle, Netezza) to modern cloud data platforms.
Databricks Technical Skillset:
Hands-on technical experience with
Databricks
(Delta Lake, PySpark, Spark SQL, Unity Catalog, Databricks Workflows). Ability to write and optimize SQL queries, audit PySpark scripts, and design lakehouse pipelines.
Domain Mastery:
In-depth domain knowledge of Corporate Banking domains, including:
Commercial Lending & Credit Risk (PD, LGD, EAD modeling data requirements)
Trade Finance & Supply Chain Finance
Global Cash Management, Payments (SWIFT ISO 20022), and Treasury Services
Basel III/IV, BCBS 239, AML, and Liquidity Risk Reporting (LCR/NSFR)
Data Modeling:
Proven expertise in conceptual, logical, and physical data modeling, source-to-target mapping, and data flow modeling.
Agile & Delivery:
Extensive experience leading Agile/Scrum delivery teams using Jira, Confluence, and enterprise business analysis frameworks.
Core Competencies & Skill Matrix
Skill Domain
Required Technical / Functional Proficiency
Level
Corporate Banking
Cash Management, Trade Finance, Commercial Loans, SWIFT, Regulatory Reporting
Subject Matter Expert
Data Warehousing
Dimensional Modeling, Star/Snowflake Schemas, EDW Migration, ETL/ELT Patterns
Master
Big Data Platform
Databricks
(Delta Lake, Unity Catalog, Spark SQL, Workspace, MLflow basics)
Advanced / Technical
Data Querying & Code
Advanced SQL (Windowing, CTEs, Performance Tuning), Python / PySpark
Advanced
Data Governance
Data Lineage, Metadata Management, Collibra / Alation, Data Auditing
Proficient
SDLC Methodology
Agile (Scrum/Kanban), Jira, Source-to-Target Mapping (STTM), UAT Strategy
Lead
Desirable / Good to Have
Databricks Certified Data Engineer Associate / Professional or Databricks Accredited Lakehouse Fundamentals.
Experience with cloud platforms (
AWS
,
Azure
, or
GCP
) hosting banking data infrastructures.
Familiarity with data streaming tools (Kafka, Spark Streaming) for real-time corporate payments and fraud detection workflows.
Togaf or CBAP (Certified Business Analysis Professional) certifications.
Position Overview
We are seeking an executive-level
Principal Data Business Analyst
to drive the modernization, architecture, and functional alignment of our Corporate Banking Enterprise Data Platform. In this high-impact role, you will bridge the gap between complex corporate banking business lines (Cash Management, Trade Finance, Lending, Treasury) and modern lakehouse data architectures.
With over 15 years of deep expertise in banking data warehousing (EDW), data modeling, and business intelligence, you will act as the technical and domain authority in migrating and optimizing end-to-end data pipelines onto
Databricks
.
Key Responsibilities
1. Corporate Banking Business & Functional Analysis
Partner with senior business stakeholders across Corporate & Investment Banking (CIB) to elicit, analyze, and formalize complex data requirements for regulatory reporting, risk management, credit risk, and commercial analytics.
Translate business logic, complex financial calculations, and domain workflows into clear functional requirement documents (FRDs), source-to-target mappings (STTM), and data dictionary definitions.
Define and govern KPIs, data lineage, and business definitions for core corporate banking products (e.g., Syndicated Loans, Letters of Credit, Wire Transfers, Nostro/Vostro accounting).
2. Databricks & Lakehouse Integration
Lead the functional design and data transformation logic for modernizing legacy banking data warehouses (Oracle, Teradata, DB2) onto modern
Databricks Lakehouse (Spark, Delta Lake, Unity Catalog)
.
Design and validate PySpark/SQL-based data transformations, aggregation logic, and feature engineering specifications within Databricks environments.
Leverage
Databricks Unity Catalog
to assist in defining automated data governance, fine-grained access control policies, and auditability mechanisms tailored to banking compliance standards.
3. Data Warehousing & Architecture Strategy
Oversee dimensional and relational data modeling initiatives (Kimball/Inmon methodologies, Third Normal Form, Star/Snowflake schemas) optimized for high-volume corporate transaction datasets.
Collaborate closely with Enterprise Data Architects and ETL/Data Engineers to design scalable, near-real-time data pipelines and batch processing schedules.
Drive end-to-end Data Quality Management (DQM), root-cause anomaly analysis, reconciliation frameworks, and User Acceptance Testing (UAT) strategies.
4. Leadership & Stakeholder Management
Act as the lead bridge between executive business leaders, regulatory compliance officers, and core technical engineering squads.
Mentor senior/junior business analysts and data engineers on domain concepts, data warehouse standards, and modern data stack paradigms.
Required Qualifications & Experience
Experience:
15+ years
of progressive experience as a Data Business Analyst, Data Architect, or Systems Analyst within global corporate banking or enterprise financial services environments.
Data Warehousing Expertise:
Deep background in enterprise data warehousing (EDW) lifecycle—from legacy relational architectures (Teradata, Oracle, Netezza) to modern cloud data platforms.
Databricks Technical Skillset:
Hands-on technical experience with
Databricks
(Delta Lake, PySpark, Spark SQL, Unity Catalog, Databricks Workflows). Ability to write and optimize SQL queries, audit PySpark scripts, and design lakehouse pipelines.
Domain Mastery:
In-depth domain knowledge of Corporate Banking domains, including:
Commercial Lending & Credit Risk (PD, LGD, EAD modeling data requirements)
Trade Finance & Supply Chain Finance
Global Cash Management, Payments (SWIFT ISO 20022), and Treasury Services
Basel III/IV, BCBS 239, AML, and Liquidity Risk Reporting (LCR/NSFR)
Data Modeling:
Proven expertise in conceptual, logical, and physical data modeling, source-to-target mapping, and data flow modeling.
Agile & Delivery:
Extensive experience leading Agile/Scrum delivery teams using Jira, Confluence, and enterprise business analysis frameworks.
Core Competencies & Skill Matrix
Skill Domain
Required Technical / Functional Proficiency
Level
Corporate Banking
Cash Management, Trade Finance, Commercial Loans, SWIFT, Regulatory Reporting
Subject Matter Expert
Data Warehousing
Dimensional Modeling, Star/Snowflake Schemas, EDW Migration, ETL/ELT Patterns
Master
Big Data Platform
Databricks
(Delta Lake, Unity Catalog, Spark SQL, Workspace, MLflow basics)
Advanced / Technical
Data Querying & Code
Advanced SQL (Windowing, CTEs, Performance Tuning), Python / PySpark
Advanced
Data Governance
Data Lineage, Metadata Management, Collibra / Alation, Data Auditing
Proficient
SDLC Methodology
Agile (Scrum/Kanban), Jira, Source-to-Target Mapping (STTM), UAT Strategy
Lead
Desirable / Good to Have
Databricks Certified Data Engineer Associate / Professional or Databricks Accredited Lakehouse Fundamentals.
Experience with cloud platforms (
AWS
,
Azure
, or
GCP
) hosting banking data infrastructures.
Familiarity with data streaming tools (Kafka, Spark Streaming) for real-time corporate payments and fraud detection workflows.
Togaf or CBAP (Certified Business Analysis Professional) certifications.
Peringatan Penting
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