- Singapore
Kerja Sepenuh Masa Databricks Engineer, Gaji tinggi SGD 8,800 di BASIL TECHNOLOGIES PTE. LTD. - Maukerja
Kongsi
Simpan
Lokasi Kerja
Penerangan Kerja
Tanggungjawab
JobRole Name: Databricks Engineer
Qualifications
3 or more years of experience
in data engineering with scalable pipelines
Strong experience designing data solutions including data modelling and distributed computing architectures
Hands-on experience with data processing jobs using
PySpark, Spark SQL, and Databricks notebooks/jobs
Experience orchestrating data pipelines with
ADF, Airflow, or similar tools
Experience with both real-time and batch data processing
Experience building pipelines on
Azure,
with
AWS
experience beneficial
Proficiency in
SQL
including window functions and performance optimization
Understanding of
DevOps tools
, Git workflows, and CI/CD pipelines
Familiarity with Scrum methodology and experience working in Scrum teams
Ability to apply Scrum practices in a practical project context
Strong problem-solving and collaborative mindset
Experience with streaming technologies such as Apache Kafka, Apache Flink, or AWS Kinesis
Ability to design and implement real-time data processing pipelines
Certification:
Databricks Certified Data Engineer Associate and Databricks Certified Data Engineer Professional (
Nice to Have NOT Mandatory)
Job Description
The Data Engineer will be responsible for designing, developing, and maintaining scalable and reliable data pipelines on Databricks and cloud platforms. The role requires integrating diverse data sources, ensuring high-quality data processing, and supporting analytics, reporting, and machine learning workloads. The role involves collaborating closely with analytics, product, and infrastructure teams to enhance the company’s data platform while adhering to best practices for governance, monitoring, and reliability.
What will you do?
Develop and maintain
ETL pipelines
for centralized data storage systems (e.g. Delta Lake).
Integrate data from databases, APIs, log files, streaming platforms, and external providers
Develop data transformation routines to clean, normalize, and aggregate data
Apply data processing techniques to handle complex or inconsistent datasets
Contribute to frameworks and best practices for code development and deployment
Implement data governance in alignment with company standards
Partner with analytics and product leaders to design and operationalize pipelines
Collaborate with infrastructure leaders to advance cloud-based data platforms
Explore new tools and techniques leveraging Azure, Databricks, or related platforms
Monitor data pipelines to detect and resolve issues promptly
Develop monitoring tools, alerts, and automated error-handling mechanisms
Analyze business requirements and identify data extraction requirements
Attend and refinement sessions with users
Develop and maintain ETL pipelines for ingestion, transformation, validation, and loading
Optimize performance and batch scheduling
Develop dashboards, reports, scorecards, and data visualizations
Perform SIT, data profiling and confirm data accuracy
Validate completeness and consistency of ETL Loads
Support UAT
and production implementation
Qualifications
3 or more years of experience
in data engineering with scalable pipelines
Strong experience designing data solutions including data modelling and distributed computing architectures
Hands-on experience with data processing jobs using
PySpark, Spark SQL, and Databricks notebooks/jobs
Experience orchestrating data pipelines with
ADF, Airflow, or similar tools
Experience with both real-time and batch data processing
Experience building pipelines on
Azure,
with
AWS
experience beneficial
Proficiency in
SQL
including window functions and performance optimization
Understanding of
DevOps tools
, Git workflows, and CI/CD pipelines
Familiarity with Scrum methodology and experience working in Scrum teams
Ability to apply Scrum practices in a practical project context
Strong problem-solving and collaborative mindset
Experience with streaming technologies such as Apache Kafka, Apache Flink, or AWS Kinesis
Ability to design and implement real-time data processing pipelines
Certification:
Databricks Certified Data Engineer Associate and Databricks Certified Data Engineer Professional (
Nice to Have NOT Mandatory)
Job Description
The Data Engineer will be responsible for designing, developing, and maintaining scalable and reliable data pipelines on Databricks and cloud platforms. The role requires integrating diverse data sources, ensuring high-quality data processing, and supporting analytics, reporting, and machine learning workloads. The role involves collaborating closely with analytics, product, and infrastructure teams to enhance the company’s data platform while adhering to best practices for governance, monitoring, and reliability.
What will you do?
Develop and maintain
ETL pipelines
for centralized data storage systems (e.g. Delta Lake).
Integrate data from databases, APIs, log files, streaming platforms, and external providers
Develop data transformation routines to clean, normalize, and aggregate data
Apply data processing techniques to handle complex or inconsistent datasets
Contribute to frameworks and best practices for code development and deployment
Implement data governance in alignment with company standards
Partner with analytics and product leaders to design and operationalize pipelines
Collaborate with infrastructure leaders to advance cloud-based data platforms
Explore new tools and techniques leveraging Azure, Databricks, or related platforms
Monitor data pipelines to detect and resolve issues promptly
Develop monitoring tools, alerts, and automated error-handling mechanisms
Analyze business requirements and identify data extraction requirements
Attend and refinement sessions with users
Develop and maintain ETL pipelines for ingestion, transformation, validation, and loading
Optimize performance and batch scheduling
Develop dashboards, reports, scorecards, and data visualizations
Perform SIT, data profiling and confirm data accuracy
Validate completeness and consistency of ETL Loads
Support UAT
and production implementation
Peringatan Penting
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