Senior Backend Data Platform Engineer

Optimum Solutions (Singapore) Pte Ltd.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Optimum Solutions (Singapore) Pte Ltd. is seeking a hands-on Data Engineer to drive the core mechanics of our enterprise data platforms.

You will independently build, optimise, and secure scalable ETL pipelines, historical data architectures, and analytical datasets, ensuring rigorous data quality across our ecosystem. Responsibilities include developing batch and streaming pipelines, data transformations for EDW and data lakes, and tuning SQL and Spark workloads, with close collaboration on

Qualifications

• Minimum 6 years of hands-on data engineering experience with independent ownership.
• Advanced proficiency in SQL, Python, Spark/PySpark, Teradata and Hadoop/Cloudera.
• Experience with ETL tools and enterprise data platforms in banking-related data operations preferred.

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 collaboration with architecture and BI teams.

Skills

SQL

Python

Spark/PySpark

Teradata

Hadoop/Cloudera

Tools

Informatica/Talend

Hive

Impala

Kafka

Airflow

APIs

Git-based version control

Job description

Optimum Solutions (Singapore) Pte Ltd. is seeking a hands-on Data Engineer to drive the core mechanics of our enterprise data platforms.

You will independently build, optimise, and secure scalable ETL pipelines, historical data architectures, and analytical datasets, ensuring rigorous data quality across our ecosystem. Responsibilities include developing batch and streaming pipelines, data transformations for EDW and data lakes, and tuning SQL and Spark workloads, with close collaboration on