Senior Data Engineer

1. Role Overview

The Senior Data Engineer is responsible for designing, building, and maintaining large-scale, secure, and high-performance data pipelines supporting critical Financial Services workloads.

The role focuses on data modernization, regulatory data aggregation, and AI/ML enablement across domains such as Core Banking, Payments, Risk, Treasury, and Regulatory Reporting.

2. Key Responsibilities

. Design, implement, and optimize ETL/ELT data pipelines using Apache Spark, PySpark, Databricks, or

Azure Synapse.

. Build and operationalize real-time streaming pipelines leveraging Kafka / Confluent / Azure Event Hubs

for risk and liquidity data.

. Integrate and transform data across Core Banking, Trade, Payments, Treasury, CRM, and Compliance

systems.

. Implement data quality, validation, and lineage controls using tools such as Great Expectations / Deequ /

dbt tests.

. Develop and maintain data models and schemas (3NF, Dimensional, Data Vault 2.0).

. Collaborate with Security and Governance teams to implement data security, masking, encryption, and

tokenization in compliance with MAS TRM / PDPA / PCI-DSS.

. Participate in data platform modernization projects (Teradata / DB2 Snowflake / Databricks /

Synapse).

. Collaborate with Data Scientists and AI Engineers to deploy ML feature stores and model-serving

pipelines.

. Support regulatory reporting (MAS 610/649) and Basel III/IV data flows.

. Maintain CI/CD pipelines for data infrastructure using Azure DevOps / Terraform / GitHub Actions.

3. Required Technical Skills

Category Tools / Technologies

Languages Python, PySpark, SQL, Scala

Data Platforms Azure Data Lake, Synapse, Databricks, Snowflake

Orchestration Apache Airflow, Azure Data Factory, dbt

Streaming Kafka, Confluent, Event Hubs

Governance Apache Atlas, Azure Purview, Collibra

Security Encryption, RBAC, Tokenization, Audit Logging

CI/CD & IaC Terraform, Azure DevOps, GitHub Actions

4. Experience and Qualifications

. 6 - 10 years of experience in data engineering, with at least 3 years in BFSI (banking, insurance, or capital

markets).

. Proven experience building real-time and batch data pipelines on Azure or AWS.

. Exposure to regulatory data models (MAS 610, Basel III, IFRS 9/17, BCBS 239).

. Familiarity with DevOps and MLOps integration.

. Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field.

. Certifications preferred: Microsoft Azure Data Engineer Associate, Databricks Data Engineer

Professional, Snowflake SnowPro Core.

5. Key Attributes

. Strong analytical and problem-solving mindset.

. Ability to work across multi-disciplinary and geographically distributed teams.

. Excellent written and verbal communication skills.

. High accountability and ownership for quality and delivery.