We are looking for a Data Engineer to build and maintain the company's analytics platform by designing robust ETL pipelines and enabling advanced analytics capabilities.
Job Responsibilities:
- Design, implement, and maintain scalable Extract, Transform, Load (ETL) / Extract, Load, Transform (ELT) pipelines on Amazon Web Services (AWS) using services such as Glue, Lambda, and Fargate.
- Model, transform, and curate data in cloud data warehouses, including Amazon Redshift, Snowflake, and BigQuery.
- Integrate and normalize order data from multiple aggregator APIs such as Shopee, GoFood, and Grab into centralized data schemas.
- Build and optimize data ingestion pipelines from transactional databases (RDS/PostgreSQL), real-time event streams (Kinesis), and third-party APIs.
- Implement data quality checks, monitoring, and alerting using tools such as Great Expectations for data validation and AWS CloudWatch for real-time monitoring and notifications.
- Collaborate with analysts and data scientists to provision feature stores, support machine learning training data, and enable ad hoc analysis.
- Design and implement automated reporting pipelines and interactive dashboards using tools such as QuickSight and Metabase to support data-driven decision making.
- Establish and promote best practices for data governance, documentation, and data security.
Job Requirements:
- Minimum bachelor's degree majoring in information technology, information systems or similar.
- 5+ years of professional experience in a Data Engineering role.
- Strong proficiency with the AWS data stack: Glue, Lambda, S3, Redshift (or equivalent warehouse), IAM, CloudWatch.
- Expertise in Python and SQL for data transformation and pipeline development.
- Experience designing and maintaining reliable, incremental data pipelines (Airflow, Step Functions, or similar).
- Solid understanding of data modeling (star/snowflake schemas) and performance tuning.
- Hands-on with API integration, batch and streaming ingestion patterns.
- Familiarity with data quality frameworks and monitoring tools.
- Advanced analytics / data science experience (statistical modeling, customer segmentation, demand forecasting).
- Experience with containerized data workflows (Docker, Kubernetes, Fargate).
- Knowledge of machine learning pipelines (SageMaker, TensorFlow Extended).
- Experience with infrastructure as code (Terraform, CloudFormation). Prior exposure to F&B or e-commerce data challenges (order volumes, seasonality, multi-channel integration).
- Excellent communication skills and ability to collaborate with non technical stakeholders
