Data Engineer

BIGTAPP PTE. LTD

Singapore, SGonsitePosted Jul 17, 2026
Posting intelligenceActively listedReposted 36×, possible evergreen/ghost posting

Skills

azure devopsdatabrickspowerbigithubpythonazurecicd

About the role

Job Summary

We are seeking a highly skilled Data Engineer with 9+ years of experience to design, build, and optimize scalable data pipelines and cloud-based data solutions. The ideal candidate will have strong expertise in Azure, Databricks, ADF, Data Engineering principles, and exposure to PowerBI in Banking domain projects.

Mandatory Skills

Azure, Databricks, ADF, Data Engineering, SQL, Power BI(basic).

Key Responsibilities

Design and build scalable ETL/ELT pipelines using Azure and Databricks.

Automate data workflows and optimize performance across data systems.

Develop and maintain data models supporting analytics and reporting.

Collaborate with cross-functional teams including BI, Data Science, and Business stakeholders.

Implement best practices in data quality, governance, and security.

Troubleshoot issues and optimize system reliability and performance.

Support BI teams with data access, semantics, and structured data layers.

Qualifications

Bachelor’s degree in Computer Science, Engineering, or related field.

Technical Skills

Azure (ADF, Data Lake, Function Apps)

Databricks (PySpark, Delta Lake)

SQL / NoSQL

CI/CD with Azure DevOps / GitHub

Power BI basics for data validation

Cloud architecture understanding

Experience with CI/CD automation, Python advanced, Banking/Financial Services domain.

Soft Skills

Excellent communication, analytical thinking, stakeholder management, problem solving, documentation ability.

Work Experience

Minimum 9 years of experience as a Data Engineer in enterprise environments.

Key Result Areas (KRA):

Timely delivery of data pipelines and models

Data quality compliance across projects

Platform optimization and stability

Cross-team collaboration effectiveness

Key Performance Indicators (KPI):

Pipeline delivery success rate

Data quality issue reduction %

System/performance improvement %

Stakeholder satisfaction score

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