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.