Job Description

Data Analyst - Corporate Banking (Regulatory Liquidity)

-----------------------------------------------------------

Role Purpose

The role focuses on designing and operationalizing

regulatory-grade data pipelines and dashboards

to support

MAS Liquidity Reporting

under

Notices 649 (LCR), 652 (NSFR), and 640 (AMR)

within Corporate Banking.





You will own the

data ingestion, transformation, and KPI computation layers

, implement

Basel-aligned rules

, and deliver

automated, drill-down dashboards

for Risk, Finance, and Regulatory stakeholders.

Key Responsibilities

#

1. Data Ingestion & Controls

Integrate data from

Treasury, ALM, Risk, Finance, and Core Banking systems

(via files, APIs, or databases). Implement

schema validation

,

timestamp freshness checks

,

control-total reconciliation

, and

exception logging

. Maintain

parameterized runs

(e.g., params.yaml ) and

immutable run logs

for audit traceability.
#

2. Transformation & Regulatory Computation

MAS 649 - LCR

Classify

HQLA Levels 1/2A/2B

; apply

haircuts

,

Level-2 cap (40%)

, and

inflow cap (75%)

. Compute

HQLA

,

30-day net cash outflows

, and

LCR ratio

; generate

stress-testing scenarios

.

MAS 652 - NSFR

Map products and tenors to

ASF/RSF buckets

and policy weights. Compute

NSFR

and identify top drivers by

product

,

tenor

, and

funding type

. Develop

what-if engines

(e.g., deposit run-off, wholesale withdrawal).

MAS 640 - AMR

Identify

eligible SG-domiciled assets

(SGS, MAS Bills, MAS deposits). Track

daily AMR ratios

against prescribed thresholds (e.g., 35%) and analyze

composition trends

.
#

3. KPI Derivation & Monitoring

Deliver

core KPIs

: LCR %, Net Outflows, HQLA Total, Level-2 Cap Utilization, NSFR %, ASF/RSF, AMR %, and Eligible Assets. Track

operational KPIs

: data latency, pipeline freshness, data-quality score, reconciliation status, and scenario deviations.
#

4. Storage, Audit & Documentation

Persist intermediate outputs in

PostgreSQL, SQL Server, Snowflake, or Parquet

with

versioned parameters

. Maintain detailed

data lineage

,

transformation dictionaries

, and

Basel/MAS policy mappings

. Develop

unit tests

for computation components (haircuts, caps, ASF/RSF logic).
#

5. Visualization & Stakeholder Enablement

Build

Power BI

or

Plotly

dashboards for

LCR, NSFR, and AMR trends

,

HQLA composition

,

cap utilization

, and

ASF vs RSF

breakdowns. Enable

drill-downs

by instrument, product, counterparty, and tenor. Conduct

walkthroughs

with Risk and Finance teams and support

audit and regulatory reviews

.

Required Skills & Qualifications

Bachelor's degree in

Statistics, Data Science, Finance, Economics, Computer Science

, or related field.

2-5 years

of experience in

data analytics or data engineering

within

banking or financial services

. Strong proficiency in

SQL

(window functions, performance tuning, incremental loads). Working knowledge of

Python (Pandas/NumPy)

or

R

for analytical computations and testing. Hands-on experience with

Power BI

or

Plotly

for dashboard development. Understanding of

corporate banking products

(loans, deposits, FX/MM, securities financing) and underlying data models. Familiarity with

Basel III/IV

and

MAS liquidity concepts

(LCR, NSFR fundamentals).

Nice to Have

Experience with

ETL/orchestration tools

: SSIS, Airflow, ADF, Glue, dbt, or Spark. Exposure to

cloud data platforms

: Azure (Synapse/ADF), AWS (Redshift/Glue), or Snowflake. Background in

data-quality frameworks

,

lineage tools

, and

parameterized pipelines

. Prior experience with

MAS 649/652/640

or

Basel liquidity stress testing

is advantageous.

Soft Skills

Strong analytical thinking and

attention to detail

under regulatory scrutiny. Excellent communicator able to translate

complex data rules into clear business logic

. * Collaborative mindset across

Risk, Finance, Treasury, and Technology

teams.