Backend Data Platform Engineer (SQL / Python /PySpark)

Optimum Solutions Pte Ltd; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Optimum Solutions Pte Ltd is seeking a hands-on Data Engineer to drive the core mechanics of our enterprise data platforms in Singapore. You will independently build, optimise, and secure scalable ETL pipelines, historical data architectures, and analytical datasets, ensuring rigorous data quality across our ecosystem.

The role emphasizes end-to-end delivery with a strong focus on data warehousing, data lakes, and banking data operations, bringing an analytical mindset and secure engineering

Qualifications

• 6+ years of hands-on data engineering experience with independent ownership of deliverables.
• Strong background in enterprise data warehousing, data lakes, and banking data operations.
• Proficiency with SQL, Python, Spark/PySpark, Teradata, and Hadoop/Cloudera ecosystems.

Responsibilities

• Pipeline Engineering: Develop and maintain batch, streaming, CDC, and API-based data pipelines and historical snapshots.
• Data Transformation: Build transformations for EDW, data lakes, and semantic layers including payments data.
• Performance & Quality: Tune SQL queries and Spark jobs with robust reconciliation and monitoring.
• SDLC Delivery: Support SIT, UAT, defect resolution, release execution, and production verification with BI teams.

Skills

SQL

Python

Spark/PySpark

Teradata

Hadoop/Cloudera

Tools

Informatica/Talend

Hive

Impala

Kafka

Airflow

APIs

Git

Job description

We are looking for a hands-on Data Engineer to drive the core mechanics of our enterprise data platforms. In this role, you will independently build, optimise, and secure scalable ETL pipelines, historical data architectures, and analytical datasets, ensuring rigorous data quality and reconciliation across our ecosystem.

Responsibilities

• Pipeline Engineering: Develop and maintain robust batch, streaming, CDC, and API-based data pipelines, historical snapshots, and SCD frameworks.
• Data Transformation: Build transformations for EDW, data lakes, and semantic layers, including specialised payment harmonisation datasets.
• Performance & Quality: Tune SQL queries and Spark jobs, implementing stringent reconciliation controls, error handling, and operational monitoring.
• SDLC Delivery: Support SIT, UAT, defect resolution, release execution, and production verification in close collaboration with architecture and BI teams.

Requirements:

• Experience: Minimum 6 years of hands-on data engineering experience, with independent ownership of development and testing deliverables.
• Technical Stack: Advanced proficiency in SQL, Python, Spark/PySpark, Teradata, and Hadoop/Cloudera ecosystems.
• Tools & Integration: Experience with Informatica/Talend, Hive, Impala, Kafka, Airflow, APIs, and Git-based version control.
• Domain Exposure: Strong background in enterprise data warehousing, data lakes, and banking data operations (Payments, Risk, Finance, or AML preferred).
• Core Competencies: Independent ownership of deliverables, analytical mindset, and a commitment to secure, high-quality engineering standards.