Senior Data Engineer

ScienTec Consulting; Singapore; On-site; SGD 60,000 - 100,000; Full time

Job summary

ScienTec Consulting in Singapore invites a Senior Data Engineer to champion automation, scalability, and best practices that accelerate our data and AI maturity. Design, build, and maintain scalable data pipelines for ingestion, transformation, and delivery, while automating ETL/ELT workflows to reduce manual intervention.

You will implement validation, versioning, and rollback, build self-healing, auto-scaling pipelines, and optimize lakehouse and warehouse architectures with Databricks,

Qualifications

• Bachelor's degree in Computer Science, Information Systems, or related field.
• 6+ years of data engineering or related infrastructure experience.
• Expert in SQL, Python; proficient with Spark, Hadoop, dbt, and Airflow.
• Strong AWS data stack knowledge (Redshift, Glue, S3, EMR, Athena, Lambda, Lake Formation).
• Familiar with Databricks, Snowflake, and MLOps tools (SageMaker, MLflow, Vertex AI).
• Skilled in data modeling, performance tuning, and cost optimization.

Responsibilities

• Design, build, and maintain scalable data ingestion, transformation, and delivery pipelines.
• Automate ETL/ELT workflows to reduce manual intervention and improve reliability.
• Implement validation, versioning, and rollback mechanisms for reliability and traceability.
• Build self-healing, auto-scaling pipelines ensuring near-zero downtime and operational resilience.
• Develop and optimize lakehouse & warehouse architectures using Databricks, Snowflake, Redshift, S3, EMR, Glue, and Lake Formation.
• Integrate monitoring, alerting, and logging (CloudWatch, Prometheus, Grafana) for proactive issue resolution.
• Build data foundations for forecasting, segmentation, retention, and KPI decomposition models.
• Create reusable feature stores, model registries, and tracking frameworks supporting the MLOps lifecycle.
• Enable AI-assisted analytics through natural language query, LLM integration, and automated insights.
• Partner with cross-functional teams to ensure data readiness aligns with business timelines.

Skills

Data engineering

Data modeling

Performance tuning

Cost optimization

Education

Bachelor's degree in Computer Science or related field

Tools

Spark

Hadoop

dbt

Airflow

SageMaker

MLflow

Vertex AI

Databricks

Snowflake

Redshift

Glue

Lake Formation

Job description

• Build & optimize a high performance data platforms that powering analytics, dashboards, and AI models
• Pioneer team to freeing Data Scientists & Analysts team from manual engineering
• Salary up to $9,000 + AWS + Bonus

We're hiring Senior Data Engineer to champion automation, scalability, and best practices that accelerate company's data and AI maturity.

Key Responsibilities:

1. Design, build, and maintain scalable, end-to-end pipelines for data ingestion, transformation, and delivery.
2. Automate ETL/ELT workflows (Airflow, Glu, Step Functions, Prefect) to eliminate manual intervention and improve reliability.
3. Implement validation, version control, and rollback mechanisms for reliability and traceability.
4. Build self-healing, auto-scaling pipelines ensuring near-zero downtime and operational resilience.
5. Develop and optimize lakehouse & warehouse architectures using Databricks, Snowflake, Redshift, S3, EMR, Glue, and Lake Formation.
6. Integrate monitoring, alerting, and logging (CloudWatch, Prometheus, Grafana) for proactive issue resolution.
7. Build data foundations for forecasting, segmentation, retention, and KPI decomposition models.
8. Create reusable feature stores, model registries, and tracking frameworks supporting the MLOps lifecycle.
9. Enable AI-assisted analytics through natural language query, LLM integration, and automated insights.
10. Partner with cross-functional teams to ensure data readiness aligns with business timelines.

Requirements:

• min. Degree in Computer Science, Information Systems, or related field
• min. 6 years & above in data engineering, pipeline design, or infrastructure operations
• Expert in SQL, Python and frameworks such as Spark, Hadoop, dbt, and Airflow
• Strong knowledge of AWS stack such as Redshift, Glue, S3, EMR, Athena, Lambda, Lake Formation
• Familiar with Databricks, Snowflake, and MLOps tools (SageMaker, MLflow, Vertex AI)
• Skilled in data modelling, performance tuning, and cost optimization