Data Engineer (Python/SQL)
Summary
Build and maintain scalable data pipelines and warehouses using Python, SQL, Spark, and cloud tools to ensure clean, accessible data for analytics and AI services.
NCS is a leading AI Tech Services company. With a 15,000-strong team across the Asia Pacific, NCS scales its platforms and capabilities to provide clients with greater agility and AI expertise across a range of industries. Embracing a strong ecosystem of global partners, NCS transforms technology services delivery combining AI with digital resilience to drive real business impact. NCS is a subsidiary of the Singtel Group.
DUTIES AND RESPONSIBILITIES:
- Design, build, and maintain scalable, secure data pipelines and storage systems; ensure data quality through ETL processes and regular checks.
- Implement policies and practices to control, optimize, and secure data assets, ensuring data integrity and accessibility.
- Develop and maintain data models, structures, and databases to meet business needs; communicate data architecture effectively.
- Develop, test, and maintain scripts and programs to automate data processing and pipelines, adhering to industry standards.
- Create and operationalize data visualization solutions to simplify complex data for stakeholders and decision-making.
- Work with cross-functional teams to gather data requirements, optimize existing processes, and deliver ad hoc reports and insights
Knowledge – Knowledgeable in the following:
- Comprehensive understanding of data manipulation tools such as pandas, dplyr, and Spark.
- In-depth knowledge of big data frameworks and tools like Apache Spark and Hadoop.
- Familiarity with data warehousing services like AWS Redshift, Snowflake, or similar solutions.
- Proficiency in AWS Cloud Services, particularly AWS Glue and AWS Lake Formation.
- Familiarity with business intelligence tools such as Tableau, Power BI, QuickSight, or Google Data Studio.
- Awareness of data visualization libraries and packages like Dash, Plotly, Matplotlib, ggplot, and Folium.
- Understanding of machine learning libraries and tools (e.g., scikit-learn, caret, MATLAB) is a plus.
- Knowledge of data governance, quality control, and security best practices.