Senior Data Engineer (SQL, AWS) - SPVL

We are looking for Senior Data Engineer to join an IT Software Datahub Company!

• Exciting opportunity to work with a team of professionals to build and optimize high-performance data pipelines and platforms powering analytics, dashboards, and AI models
• Permanent position with great career exposure
• Work location: Central

Overview

As a Senior Data Engineer, you will design, build, and optimize high-performance data pipelines and platforms powering analytics, dashboards, and AI models across the enterprise. Your mission is to deliver accessible, reliable, and production-ready data—freeing Data Scientists and Analysts from manual engineering. You will champion automation, scalability, and best practices that accelerate the company’s data and AI maturity

Responsibilities

1. Data Pipeline Engineering & Automation

• Design, build, and maintain scalable, end-to-end pipelines for data ingestion, transformation, and delivery.
• Automate ETL/ELT workflows (Airflow, Glue, Step Functions, Prefect) to eliminate manual intervention and improve reliability.
• Implement validation, version control, and rollback mechanisms for reliability and traceability.
• Build self-healing, auto-scaling pipelines ensuring near-zero downtime and operational resilience.

2. Data Infrastructure & Performance Optimization

• Develop and optimize lakehouse and warehouse architectures using Databricks, Snowflake, Redshift, S3, EMR, Glue, and Lake Formation.
• Apply best practices in data partitioning, indexing, and caching to improve query speed and control compute costs.
• Integrate monitoring, alerting, and logging (CloudWatch, Prometheus, Grafana) for proactive issue resolution.
• Collaborate with the Data Architect to ensure scalability, efficiency, and alignment with enterprise standards.

3. AI & Analytics Enablement

• Build data foundations for forecasting, segmentation, retention, and KPI decomposition models.
• Partner with Data Scientists to develop model-serving pipelines with automated retraining and versioning.
• Create reusable feature stores, model registries, and tracking frameworks supporting the full MLOps lifecycle.
• Enable AI-assisted analytics through natural language query, LLM integration, and automated insights.

4. Data Quality, Governance & Documentation

• Maintain detailed documentation of pipelines, lineage, and metadata.
• Enforce access control, encryption, and compliance with PDPA, GDPR, and internal governance.
• Develop automated quality checks, anomaly detection, audit trails to ensure trust in data.
• Deliver data that is ready for consumption—without revalidation or major manual cleanup.

Requirements

• Min 5 years in data engineering, pipeline design, or infrastructure operations.
• Proven experience managing large-scale (multi-terabyte) datasets with high uptime.
• Expert in SQL, Python, and frameworks such as Spark, Hadoop, dbt, and Airflow.
• Strong knowledge of AWS stack (Redshift, Glue, S3, EMR, Athena, Lambda, Lake Formation).
• Familiar with Databricks, Snowflake, and MLOps tools (SageMaker, MLflow, Vertex AI).
• Certification in AWS Certified Data Engineer will be an added advantage.

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Liew Chien Hui - R2090138
ScienTec Consulting Pte Ltd - 11C5781