Databricks Data Engineer – Scalable Pipelines & Cloud

Alphaeus Pte Ltd; Singapore; On-site; SGD 90,000 - 170,000; Full time

Job summary

Alphaeus Pte Ltd is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines on Databricks and cloud platforms. You will work with analytics, product, and infrastructure teams to enable enterprise data, reporting, and analytics initiatives.

Responsibilities include developing ETL pipelines, data ingestion and validation, and implementing governance, monitoring, and reliability standards across batch and real-time processing environments.

Qualifications

• 3+ years of Data Engineering experience.
• Proficient with Databricks, PySpark, Spark SQL and Databricks Notebooks/Jobs.
• Experience with orchestration tools (ADF, Airflow) and DevOps practices.
• Knowledge of streaming tech such as AWS Kinesis.

Responsibilities

• Develop and maintain ETL pipelines for centralized data platforms (Delta Lake).
• Build ingestion, transformation, validation, and loading processes.
• Integrate data from databases, APIs, streaming platforms, logs, and external providers.
• Design and optimize scalable data processing solutions.
• Implement data governance, monitoring, and reliability standards.
• Develop monitoring tools, alerts, and automated error-handling mechanisms.
• Support SIT, UAT, data profiling, validation, and production deployment.
• Collaborate with business, analytics, product, and infrastructure stakeholders to deliver data solutions.

Skills

Databricks

PySpark

Spark SQL

Databricks Notebooks & Jobs

Distributed Computing

SQL Performance

ADF / Airflow

Batch & Real-Time

Git / CI/CD / DevOps

AWS Kinesis

Tools

Azure Data Factory

Airflow

Job description

Alphaeus Pte Ltd is seeking an experienced Data Engineer to design, build, and maintain scalable data pipelines on Databricks and cloud platforms. You will work with analytics, product, and infrastructure teams to enable enterprise data, reporting, and analytics initiatives.

Responsibilities include developing ETL pipelines, data ingestion and validation, and implementing governance, monitoring, and reliability standards across batch and real-time processing environments.