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. By submitting your resume, you consent to the collection, use, and disclosure of your personal information per ScienTec’s Privacy Policy (scientecconsulting.com/privacy-policy). This authorizes us to: Contact you about potential opportunities. Delete personal data not required at this application stage. To withdraw consent, email dpo@scientecconsulting.com. All applications will be processed with strict confidence. Only shortlisted candidates will be contacted. Liew Chien Hui - R2090138 ScienTec Consulting Pte Ltd - 11C5781