Principal Data Business Analyst

KSYS SOLUTIONS PTE. LTD.

Singapore

On-site

SGD 180,000 - 260,000

Full time

2 days ago

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Job summary

KSYS SOLUTIONS PTE. LTD. seeks an executive-level Principal Data Business Analyst to drive modernization of the Corporate Banking Enterprise Data Platform.

You will bridge banking business lines with a Databricks Lakehouse, overseeing data governance, modeling, and end-to-end data pipelines across regulatory reporting, risk, and analytics. You will lead functional design, FRDs, mappings, and KPI definitions while mentoring analysts and collaborating with enterprise architects to deliver scalable

Qualifications

15+ years of progressive experience in data/business analytics or data architecture in corporate banking or financial services.
Deep EDW/ data warehousing experience across legacy to cloud platforms.
Hands-on Databricks experience with Delta Lake, PySpark, Spark SQL, Unity Catalog.

Responsibilities

Lead corporate banking data requirements elicitation and FRDs, data dictionary definitions, and source-to-target mappings.
Architect and govern Databricks lakehouse integration of legacy warehouses (Oracle/Teradata/DB2) to Databricks.
Oversee dimensional modeling and data pipeline design for high-volume banking data and real-time/batch processing.
Provide leadership to BI/data engineering teams and align with senior stakeholders on regulatory reporting and risk analytics.

Skills

Databricks

SQL

PySpark

Data Modeling

Data Governance

Agile Delivery

Stakeholder Management

Tools

Databricks

Teradata

Oracle

DB2

Job description

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

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).
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.
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.
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