Job Description

Singapore Onsite IT

Key Responsibilities

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Design, build, and maintain scalable data pipelines for batch and real-time processing using Python, SQL, ETL/ELT frameworks, and big-data technologies. Participate in end-to-end data project delivery using SDLC, Agile, or hybrid development methodologies. Work closely with business and technology stakeholders to understand data requirements related to banking products, transactions, customer analytics, and regulatory reporting. Develop efficient normalized and de-normalized data models for operational and analytical workloads. Design and manage data warehouses, data marts, and integration layers aligned with enterprise data architecture. Deploy physical data models and optimize performance for large-scale financial datasets. Ensure adherence to data governance, quality, metadata, and privacy standards across all solutions. Produce and maintain data documentation including dictionaries, lineage diagrams, and technical specifications. Support data lineage, metadata management, and data quality initiatives to improve transparency and trust. Provide data-driven assistance to business users and proactively communicate technical challenges. Present insights, designs, and concepts effectively to both technical and non-technical stakeholders.

Skills/Experience:

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Technical Skills

Proficient in Python; experienced with Spark for scalable ETL/ELT pipelines. Strong SQL experience with large-scale datasets and warehouse solutions. Knowledge of Hadoop ecosystem tools such as Hive, Spark, and HDFS. Experience with AWS services including Glue, Redshift, RDS, S3, and basic IAM/VPC/security configurations. Hands-on Linux skills, shell scripting, and AWS CLI usage. Ability to work across SQL, NoSQL, and data lake environments. Exposure to Terraform, Talend, or similar tools is a plus. Familiarity with visualization tools such as QuickSight, Qlik, or Tableau. Ability to write clean, production-grade code and maintain clear pipeline documentation.

Experience

Experience with large datasets on platforms such as Greenplum, Hadoop, Oracle, DB2, or MongoDB. Familiarity with dashboarding tools (Tableau, Power BI, SAS VA). Experience in scripting, application packaging, and deployment across DEV-PROD environments. Understanding of change management, service request processes, and maintenance reporting. Strong data modelling capabilities (logical and physical) for banking, risk, compliance, and analytics use cases. Deep knowledge of relational/dimensional modelling, data warehousing concepts, and data integration techniques. Strong SQL expertise supporting large and complex financial data environments.

Education & Certifications

Bachelor's degree in Software Engineering, Computer Science, or equivalent experience. Professional cloud certifications (AWS/Azure/GCP) are preferred, including: + AWS Certified Data Analytics - Specialty
+ AWS Solutions Architect - Associate
+ Azure Data Engineer Associate
+ Google Professional Data Engineer
+ Databricks Data Engineer Associate/Professional
+ Cloudera Certified Data Engineer